conversations listlengths 4 8 | system stringclasses 1
value | tools stringclasses 14
values |
|---|---|---|
[
{
"from": "human",
"value": "Investigate the potential of the drug S(=O)(=O)(N1C(C)=C(C(=O)N[C@H]([C@H](O)C[NH2+]C2CC2)Cc2ccccc2)[C@@H](C)C(C(=O)NOCc2ccccc2)=C1C)C and Clc1cc2nc([nH]c2cc1)N to block BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Examine the drug's FC(F)Oc1ccc(cc1)[C@@]1(N=C(N)N(C)C1=O)c1cc(ccc1)C#C and S(=O)(=O)(N(C)c1cc2cc(NCCCCOc3cc(CC(NC2=O)C(O)C[NH2+]C2(CC2)c2cc(ccc2)C(C)C)ccc3)c1)CCC ability to inhibit BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Assess the drug's Fc1ccc(NC(=O)c2ncc(cc2)C#N)cc1[C@]1(N=C(OCC1)N)C and Fc1cc(cc(F)c1)CC(NC(=O)c1c2cccnc2n(c1)C(=O)N(CCCC)C)C(O)C1[NH2+]Cc2c(C1)cccc2 inhibitory activity on BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": ... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Does the drug Fc1c2c(ccc1)C(N=C2N)(C=1C=C(C)C(=O)N(C=1)C)c1cc(ccc1)-c1cncnc1 and Fc1ccc(NC(=O)c2ncc(cc2)C#N)cc1[C@]1(N=C(OCC1)N)C block BACE1 activity? MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Can the drug O(C)c1cc(ccc1)-c1cc(ccc1)[C@]1(N=C(N)N(C)C(=O)C1)C and Clc1cc(C[NH+]2CCC(NC(=O)COc3ccc(S(=O)(=O)N)cc3)CC2)c(OC(C)C)c(OC)c1 inhibit the enzyme BACE1? MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_p... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Test the drug FC(F)Oc1ccc(cc1)[C@@]1(N=C(N)N(C)C1=O)c1cc(ccc1)C#CCC and O(C)c1cc(ccc1)C[NH2+]CC(O)C(NC(=O)c1cc(N2CCCC2=O)c2NC(=O)N(c2c1)CC)Cc1ccccc1 for inhibition of BACE1 activity. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Predict the drug's O1c2c(cc(cc2)-c2cncnc2)C2(N=C(N)N(C)C2=O)CC1(C)C and S(=O)(=O)(N(C)c1cc(cc(c1)C(=O)N[C@H]([C@@H](O)CC(=O)N[C@@H](C(C)C)C(=O)Nc1cc(cc(c1)C(=O)[O-])C(=O)[O-])COc1cc(F)cc(F)c1)C(=O)N[C@H](C)c1ccccc1)C cytostatic potential against BACE1. MemoryPool(arguments=dict_... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Examine if the drug Fc1ncccc1-c1cc(ccc1)C1(N=C(N)N(C)C1=O)c1cn(nc1)C1CCCC1 and Fc1c2c(ccc1)C(N=C2N)(C=1C=C(C)C(=O)N(C=1)CC)c1cc(ccc1)-c1cncnc1 shows cytostatic activity against BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"nam... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Evaluate if the drug O(C)c1cc(ccc1)C(=O)Nc1cc(ccc1)C1(N=C(N)N(C)C1=O)C1CCCCC1 and Fc1cc(cc(F)c1)C[C@H](NC(=O)C)[C@H](O)C[NH2+]C(C)(C)c1cc(ccc1)C(C)(C)C blocks the activity of BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Determine if the drug Fc1cc(cc(F)c1)CC(NC(=O)c1c2cccnc2n(c1)C(=O)N(CCCC)C)C(O)C1[NH2+]Cc2c(C1)cccc2 and Clc1cc2CC(N=C(NC(Cc3cscc3C#C)C(=O)[O-])c2cc1)(C)C has the potential to inhibit BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Investigate if the drug Clc1ccccc1-c1n(Cc2nc(N)c(OCCO)cc2)c(cc1)-c1ccc(Oc2cncnc2)cc1 and O(c1cc(ccc1OC)C1(N=C(N)N(C)C1=O)c1cc(ccc1)-c1cccnc1)C1CCCC1 inhibit the activity of BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\":... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Analyze if the drug O=C1N(Cc2ccc(cc2)CNC(=O)NCCCC)C(NC1(CC1CCCCC1)C)=N and S(=O)(=O)(N1C(C)=C(C(=O)N[C@H]([C@H](O)C[NH2+]C2CC2)Cc2ccccc2)[C@@H](C(C)C)C(C(=O)NOCc2ccccc2)=C1C)C is a candidate for BACE1 inhibition. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "functio... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Predict the likelihood of the drug FC1(F)COC(=NC1(C)c1cc(NC(=O)c2ncc(OC(F)(F)F)cc2)ccc1F)N and S1(=O)(=O)N(c2cc(cc3n(cc(CC1)c23)CC)C(=O)NC([C@H](O)C[NH2+]CC#C)Cc1ccccc1)C inhibiting BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Assess the potential of the drug S(Oc1cc(ccc1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]C(C(=O)NC1CCCCC1)C)(=O)(=O)Cc1ccccc1 and Fc1ncccc1-c1cc(ccc1)[C@]1([NH+]=C(N2CC(N=C12)(C)C)N)c1ccc(OC(F)(F)F)cc1 to inhibit BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call"... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Determine if the drug S(=O)(=O)(N(c1cc(cc(NCC)c1)C(=O)N[C@H]([C@H](O)C[NH2+][C@H](C(=O)NC1CCCCC1)C)Cc1ccccc1)c1ccccc1)C and Clc1cc2CC(N=C(NC(Cc3ccccc3)c3ncccn3)c2cc1)(C)C acts as an inhibitor of BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Does the drug FC1(F)CN2C(=NC1)[C@]([NH+]=C2N)(c1cc(ccc1)C#CCOC)c1ccc(OC(F)F)cc1 and S(=O)(=O)(N(Cc1ccccc1)c1cc(ccc1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]C(C(=O)NC1CCCCC1)C)C prevent BACE1 activity? MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Test the cytostatic potential of the drug O=C(N)CC[C@H](N(C(=O)[C@@H](NC(=O)[C@@H](O)C)C(C)C)C)C(=O)N[C@H]([C@@H](O)CC(=O)N[C@@H](CC(C)C)C(=O)N[C@H](C(=O)N([C@H](CC1CCCCC1)C(=O)[O-])C)C)CC1CCCCC1 and FC(F)(F)Oc1cc(ccc1)-c1cc2c(Oc3c(cc(OC)cc3)C23N=C(OC3)N)cc1 against BACE1. Memor... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Evaluate if the drug O1c2ncc(cc2[C@@H]([NH2+]C[C@@H](O)[C@@H](NC(=O)[C@H](OC)C)Cc2cc3OCOc3cc2)CC12CCC2)CC(C)(C)C and Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCC(N(O)C(=O)C)CC1)c1cc(ccc1)C(C)(C)C has cytostatic effects on BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
"f... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Analyze whether the drug S1(=O)(=O)N(c2cc(cc3c2n(cc3CC)CC1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]C1CCCCC1)CC and O1CC2(N=C1N)c1cc(ccc1Oc1c2cc(OC)cc1)-c1cc(ccc1)C blocks BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_prope... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Predict if the drug S1(=O)(=O)CC(Cc2cc(OCCC)ccc2)C(O)C([NH2+]Cc2cc(ccc2)C(C)(C)C)C1 and S(=O)(=O)(Nc1cc(ccc1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]C(C(=O)NC1CCCCC1)C)c1ccccc1 is effective at inhibiting BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Investigate the ability of the drug S1(=O)(=O)CC(Cc2cc(OC(C(F)(F)F)C(F)(F)F)c(N)c(F)c2)C(O)C([NH2+]Cc2cc(ccc2)C(C)(C)C)C1 and S(=O)(=O)(N(C)c1cc(cc(c1)C(OCC(O)(Cc1ccccc1)C[NH3+])=O)C(=O)NC(C)c1ccc(F)cc1)C to inhibit BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
"from"... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Examine the drug S(=O)(=O)(N(C)c1cc(cc(c1)C(=O)NC(C(O)CC(OC)COC)COc1cc(F)cc(F)c1)C(=O)NC(C)c1ccccc1)C and Clc1cc(ccc1)-c1cc(ccc1)C1(N=C(OC1)N)c1ccc(OC)cc1 for inhibition of BACE1 activity. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Test whether the drug O=C1N(C)C(=NC(C1)(C)C1CC1c1cc(ccc1)CCc1ccccc1)N and FC(F)(F)c1cc(ccc1)C[NH2+]C[C@@H](O)[C@@H](NC(=O)c1cc(N2CCCC2=O)cc(NCC)c1)Cc1ccccc1 acts as a BACE1 inhibitor. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"nam... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Assess if the drug S(=O)(=O)(N(C)c1cc2cc(c1)C(=O)NC(COCc1cc(ccc1)C(NC2=O)c1ccccc1)C(O)CC(C(C)C)C(=O)NC)C and Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCCCC1)c1cc(N(C)C)ccc1 demonstrates activity against BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_cal... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Determine the drug's Clc1cc(C[NH+]2CCC(NC(=O)COc3ccc(S(=O)(=O)N)cc3C)CC2)c(OC(C)C)c(OC)c1 and S(=O)(=O)(N(C)c1cc(cc(c1)C(OCC(O)(Cc1ccccc1)C[NH3+])=O)C(=O)NC(C)c1ccc(F)cc1)C role in inhibiting BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Analyze the drug's O1CCCCNc2cc(cc(c2)C(=O)NC(Cc2cc1ccc2)C(O)C[NH2+]C1(CCCC1)c1cc(ccc1)C(C)(C)C)COC and Fc1cc(cc(F)c1)CC(NC(=O)c1cc(cc(Oc2ccc(F)cc2)c1)C(=O)N(CCC)CCC)C(O)C[NH2+]Cc1cc(OC)ccc1 potential for BACE1 inhibition. MemoryPool(arguments=dict_keys([]))"
},
{
"from":... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Evaluate the drug IC1=CN=C(NC1=O)C(NC1=NC(Cc2c1ccc(Cl)c2)(C)C)Cc1ccccc1 and FC(F)Oc1ccc(cc1)[C@@]1(N=C(N)N(C)C1=O)c1cc(ccc1)CCCCC for BACE1 cytostatic effects. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_pre... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Check if the drug Fc1cc(cc(F)c1)C[C@H](NC(=O)C)[C@H](O)C[NH2+]Cc1cc(ccc1)C(C)(C)C and OC(C(NC(=O)c1cc(Nc2ccccc2)ccc1)Cc1ccccc1)C[NH2+]C(C(=O)NC1CCCCC1)C is an effective BACE1 inhibitor. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"n... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Predict the drug's O=C1N(C)C(=NC(=C1)CCc1ccc(cc1)-c1ccccc1)N and Clc1scc(c1)C1(N=C(N)N(C)C1=O)c1cc(ccc1)-c1cncnc1 effect on BACE1 inhibition. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"argum... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Test whether the drug O(C(=O)C1[NH2+]CC2(C1)c1c(NC2=O)cccc1)C1CCN(CC1)C(=O)C and O1CCCCCCNC(=O)CC(NC(=O)C(NC1=O)C(C)C)C(=O)NC(C(O)CC(C(=O)NC(C(C)C)C(=O)NCc1ccccc1)C)CC(C)C blocks the activity of BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Determine the potential cytostatic activity of the drug Clc1ccc(nc1)C(=O)Nc1cc(ccc1)[C@]1([NH+]=C(N)CN(C)C1=O)C and O(CC1CC1)c1cc(ccc1)-c1ccc(cc1)C1CC1C=1N=C(N)N(C)C(=O)C=1 on BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Investigate whether the drug S1CCC(=NC1(C)c1cc(ccc1)-c1cc(OC)ccc1)N and O=C1N(Cc2ccc(cc2)CNC(=O)NCCCC)C(NC1(CC1CCCCC1)C)=N is an inhibitor of BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", ... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Does the drug Fc1cc(cc(F)c1)C[C@H](NC(=O)C)[C@H](O)C[NH2+]C1(CC1)c1cc(ccc1)C(CF)(C)C and O(C(C)C)c1cc(ccc1OC)C1(N=C(N)N(C)C1=O)c1cc(ccc1)-c1cccnc1 inhibit the enzyme activity of BACE1? MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"na... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Is the drug capable of inhibiting BACE1? MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
},
{
... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Test the drug for its ability to inhibit BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
},... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Analyze the effect of the drug on BACE1 activity. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Investigate whether the drug acts as a BACE1 inhibitor. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Predict the drug's action on BACE1 inhibition. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
},
... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Assess the likelihood of the drug inhibiting BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Determine the drug's inhibitory effect on BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
}... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Test whether the drug has activity against BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Examine if the drug demonstrates BACE1 inhibition. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Predict if the drug is a potential BACE1 inhibitor. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Can the drug act as a BACE1 inhibitor? MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
},
{
... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Investigate if the drug blocks BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
},
{
"... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Analyze the likelihood of the drug inhibiting BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Test if the drug inhibit the BACE1 enzyme. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
},
{
... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Assess whether the drug has an cytostatic effect on BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Clas... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Determine the drug's ability to block BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
},
... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Predict if the drug demonstrates activity against BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Investigate the potential of the drug to block BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Examine the drug's ability to inhibit BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
},
... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Assess the drug's inhibitory activity on BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
},... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Does the drug block BACE1 activity? MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
},
{
"fr... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Can the drug inhibit the enzyme BACE1? MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
},
{
... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Test the drug for inhibition of BACE1 activity. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
},... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Predict the drug's cytostatic potential against BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Examine if the drug shows cytostatic activity against BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Cl... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Evaluate if the drug blocks the activity of BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Determine if the drug has the potential to inhibit BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Investigate if the drug inhibit the activity of BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Analyze if the drug is a candidate for BACE1 inhibition. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Predict the likelihood of the drug inhibiting BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Assess the potential of the drug to inhibit BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Determine if the drug acts as an inhibitor of BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Does the drug prevent BACE1 activity? MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
},
{
"... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Test the cytostatic potential of the drug against BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Evaluate if the drug has cytostatic effects on BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Analyze whether the drug blocks BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
},
{
... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Predict if the drug is effective at inhibiting BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Investigate the ability of the drug to inhibit BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Examine the drug for inhibition of BACE1 activity. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Test whether the drug acts as a BACE1 inhibitor. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
}... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Assess if the drug demonstrates activity against BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Determine the drug's role in inhibiting BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
},
... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Analyze the drug's potential for BACE1 inhibition. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Evaluate the drug for BACE1 cytostatic effects. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
},... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Check if the drug is an effective BACE1 inhibitor. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Predict the drug's effect on BACE1 inhibition. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
},
... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Test whether the drug blocks the activity of BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Determine the potential cytostatic activity of the drug on BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\":... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Investigate whether the drug is an inhibitor of BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Does the drug inhibit the enzyme activity of BACE1? MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Assess the generated drugs for inhibitory activity against BACE1. MemoryPool(arguments=dict_keys(['output_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_nam... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Determine if the generated drugs inhibit BACE1. MemoryPool(arguments=dict_keys(['output_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"Class\"}}"
... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Evaluate whether the optimized drugs can inhibit BACE1. MemoryPool(arguments=dict_keys(['output_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"Cla... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Analyze the inhibitory potential of the optimized drugs on BACE1. MemoryPool(arguments=dict_keys(['output_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_nam... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Predict whether the optimized drugs inhibit BACE1. MemoryPool(arguments=dict_keys(['output_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"Class\"}... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Examine if the generated drugs acts as a BACE1 inhibitor. MemoryPool(arguments=dict_keys(['output_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"C... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Investigate whether the generated drugs inhibit BACE1 activity. MemoryPool(arguments=dict_keys(['output_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Test the optimized drugs for inhibition of BACE1. MemoryPool(arguments=dict_keys(['output_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"Class\"}}... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Identify if the generated drugs inhibit BACE1. MemoryPool(arguments=dict_keys(['output_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"Class\"}}"
... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Assess if the optimized drugs is a BACE1 inhibitor. MemoryPool(arguments=dict_keys(['output_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"Class\"... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Does the generated drugs inhibit BACE1? MemoryPool(arguments=dict_keys(['output_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"Class\"}}"
},
{... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Is the generated drugs an inhibitor of BACE1? MemoryPool(arguments=dict_keys(['output_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"Class\"}}"
... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Evaluate if the generated drugs blocks BACE1 activity. MemoryPool(arguments=dict_keys(['output_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"Clas... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Predict the inhibition of BACE1 by the optimized drugs. MemoryPool(arguments=dict_keys(['output_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"Cla... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Determine whether the generated drugs inhibit BACE1. MemoryPool(arguments=dict_keys(['output_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"Class\... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Analyze the potential of the generated drugs as a BACE1 inhibitor. MemoryPool(arguments=dict_keys(['output_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_na... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Investigate if the optimized drugs can inhibit BACE1. MemoryPool(arguments=dict_keys(['output_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"Class... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
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