conversations listlengths 4 8 | system stringclasses 1
value | tools stringclasses 14
values |
|---|---|---|
[
{
"from": "human",
"value": "How likely is the drug to exhibit lipophilic behavior? MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}... | 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": "How lipophilic is the molecule compared to other substances? MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"li... | 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": "How does the drug behave in nonpolar environments? MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
... | 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": "How does the drug behave in fat-based solvents? MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
},
... | 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 you measure the lipophilicity of the drug? MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
},
... | 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": "How soluble is the molecule in lipophilic environments? MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}... | 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 how lipophilic the compound is. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
},
{
... | 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": "How lipophilic is the drug based on its chemical structure? MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lip... | 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": "What is the drug's potential to be absorbed into lipid environments? 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": "Estimate the drug's affinity for lipid tissues or areas. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"... | 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": "How does the drug interact with nonpolar solvents or lipids? MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"li... | 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 you assess the lipophilicity of the molecule? MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
}... | 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": "What is the molecule's solubility in lipophilic environments? MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"l... | 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": "How lipophilic is the generated drugs on a scale from low to high? 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": "How can the generated drugs' lipophilicity be predicted? MemoryPool(arguments=dict_keys(['output_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"li... | 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": "How does the generated drugs behave in hydrophobic systems? 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": "Forecast the lipophilicity of the compound. MemoryPool(arguments=dict_keys(['output_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"lipo\"}}"
},
... | 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": "Estimate the optimized drugs' solubility in oil-based environments. MemoryPool(arguments=dict_keys(['output_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_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 solubility of the generated drugs in lipid-rich environments. MemoryPool(arguments=dict_keys(['output_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"prop... | 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": "What is the generated drugs' likelihood of being soluble in fats? 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": "How likely is the molecule to partition into lipophilic areas? 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": "Predict the molecule' potential for lipid dissolution. MemoryPool(arguments=dict_keys(['output_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"lipo... | 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": "How does the optimized drugs behave in lipid phases? MemoryPool(arguments=dict_keys(['output_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"lipo\"... | 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": "What is the generated drugs' solubility in lipid and nonpolar solvents? MemoryPool(arguments=dict_keys(['output_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"proper... | 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": "Calculate the lipophilicity of the optimized drugs. MemoryPool(arguments=dict_keys(['output_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"lipo\"}... | 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 how lipophilic the compound is. MemoryPool(arguments=dict_keys(['output_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"lipo\"}}"
},
... | 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 how well the molecule mixes with lipids. MemoryPool(arguments=dict_keys(['output_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"lipo\"}}"
... | 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": "Estimate the generated drugs' distribution in lipid-based systems. 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": "Evaluate how lipophilic the molecule is. MemoryPool(arguments=dict_keys(['output_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"lipo\"}}"
},
{... | 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": "How does the optimized drugs' polarity affect its lipophilicity? 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": "Can you estimate the lipid solubility of the molecule? MemoryPool(arguments=dict_keys(['output_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"lipo... | 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": "How does the compound behave in terms of lipophilicity? MemoryPool(arguments=dict_keys(['output_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"lip... | 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 how the generated drugs would behave in fat-rich environments. MemoryPool(arguments=dict_keys(['output_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"propert... | 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 O(C)c1ccccc1-c1cc(ccc1)C1CC1C1(N=C(N)N(C)C(=O)C1)C for inhibitory activity against BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"O(C)c1cc... | 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 Clc1ccc(nc1)C(=O)Nc1cc(C2(N=C(N)COCC2)C)c(F)cc1 inhibits BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"Clc1ccc(nc1)C(=O)Nc1cc(C2(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": "Evaluate whether the drug FC(F)(F)Oc1cc(ccc1)-c1cc2c(Oc3c(cc(OC)cc3)C23N=C(OC3)N)cc1 can inhibit BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"FC(F)(F)Oc... | 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 drug O(C)c1ccc(cc1)C1(N=C(C)C(=N1)N)c1cc(ccc1)-c1cncnc1 on BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"O(C)c1cc... | 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 drug O(C(C)C)C(=O)CN1C(C)=C(C(=O)N[C@H]([C@H](O)C[NH2+]C2CC2)Cc2ccccc2)[C@@H](C)C(C(=O)NOCc2ccccc2)=C1C inhibits BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"argumen... | 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 S1(=O)(=O)Nc2cc(cc3c2n(cc3CC)CCC1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]C1CC1 acts as a BACE1 inhibitor. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": ... | 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 Brc1ccc(cc1)COC(=O)C1[NH2+]CC2(C1)c1c(NC2=O)cccc1 inhibits BACE1 activity. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"Brc1ccc(cc... | 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 s1ccnc1-c1cc(ccc1)C[C@H](NC(=O)[C@H](OC)C)[C@H](O)C[NH2+][C@H]1CC2(Oc3ncc(cc13)CC(C)(C)C)CCC2 for inhibition of BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {... | 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 drug Clc1cc(C)c(nc1)C(=O)Nc1cc(C2(N=C(OCC2(F)F)N)C)c(F)cc1 inhibits BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"Clc1cc(C)c(nc1)C(=O)Nc1... | 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 O=C1N(CC(=NC1(C)c1cc(NC(=O)c2ncc(cc2)C#N)ccc1)N)C is a BACE1 inhibitor. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"O=C1N(CC(=NC1(C)c1cc(NC... | 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 O=C(NC(=[NH2+])N)Cn1c(ccc1-c1ccccc1)-c1ccccc1 inhibit BACE1? MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"O=C(NC(=[NH2+])N)Cn1c(ccc1-c1ccccc1)-c1... | 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 Fc1cc(cc(F)c1)CC(NC(=O)c1cc(cc(c1)/C(=N\\OCc1ccccc1)/C)C(=O)N(CCC)CCC)C(O)C[NH2+]Cc1cc(OC)ccc1 an inhibitor of BACE1? MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"d... | 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 S(=O)(=O)(N(C)c1cc(cc(c1)/C(=N\\OCCC)/C)C(=O)NC([C@@H](O)C[NH2+]Cc1cc(OC)ccc1)Cc1cc(F)cc(F)c1)C blocks BACE1 activity. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"argumen... | 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 drug S1(=O)C[C@@H](Cc2cc(O[C@H](COC)C(F)(F)F)c(N)c(F)c2)[C@H](O)[C@@H]([NH2+]Cc2cc(ccc2)C(C)(C)C)C1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"argumen... | 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 drug Fc1cc(ccc1OC)[C@H](Cc1cc([N+](=O)[O-])ccc1OC)c1nc([nH]c1)N inhibits BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"Fc1cc(ccc1OC... | 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 drug O(C)c1cc(ccc1)-c1cc(ccc1)C1(N=C(N)CN1C(=O)C)C as a BACE1 inhibitor. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"O(C)c1cc(ccc... | 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 O(C(=O)C1=CN(C=C(C(=O)N[C@H]([C@H](O)C[NH2+]C2CC2)Cc2ccccc2)[C@@H]1C)CC(OC(C)C)=O)C(C)(C)C can inhibit BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": ... | 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 BACE1 inhibitory activity of the drug n1cc(ccc1)-c1cc(ccc1)C1(N=C(N2C1=NCCC2)N)c1ccncc1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"n1cc(ccc1)-c1... | 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 whether the drug O1c2ncc(cc2C([NH2+]CC(O)C(NC(=O)C)Cc2cc3OCCOc3cc2)CC12CCC2)CC(C)(C)C inhibits BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"O1c2nc... | 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 S(=O)(=O)(N(C)c1cc(cc(c1)COCC([NH3+])Cc1ccccc1)C(=O)NC(C)c1ccc(F)cc1)C ability to inhibit BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": ... | 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 Fc1cc(cc(F)c1)C[C@H](NC(=O)c1cc(cc(c1)C)C(=O)N(CCC)CCC)[C@@H](O)C[NH2+]Cc1cc(OC)ccc1 inhibits BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smi... | 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 BACE1 inhibitory activity of the drug O(C)c1ccc(cc1C)C1(N=C(C)C(=N1)N)c1cc(ccc1)-c1cncnc1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"O(C)c1ccc... | 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 inhibition of BACE1 by the drug 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. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"argumen... | 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 FC(F)(F)c1cc(ccc1)C[NH2+]CC(O)C(NC(=O)c1cc(N2CCCC2=O)c2c(n(cc2)CC)c1)Cc1ccccc1 effective at inhibiting BACE1? MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smil... | 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 FC(F)Oc1ccc(cc1)[C@@]1(N=C(N)N(C)C1=O)c1cc(ccc1)CCC inhibits BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"FC(F)Oc1ccc(cc1)[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": "Does the drug O=C(NCC1CCCCC1)C(Cc1cc2cc(ccc2nc1N)-c1ccccc1C)C show inhibitory activity against BACE1? MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"O=C(NCC1CCCC... | 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 S1CC(=NC1(C)c1cc(ccc1)-c1cc(OC)ccc1)N has inhibitory effects on BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"S1CC(=NC1(C)c1cc(ccc1)-c1c... | 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)NCC2(N1c1cc(F)ccc1)CC([NH+](CC2)Cc1ccccc1)C can block BACE1 activity. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"S1(=O)(=O)NCC2... | 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 O=C1N(C)C(=NC(=C1)CCc1ccc(cc1)-c1ccccc1)N blocks BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"O=C1N(C)C(=NC(=C1)CCc1ccc(cc1)-c1c... | 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 Fc1cc(cc(F)c1)C[C@H](NC(=O)C)[C@H](O)C[NH2+]C1(CC1)c1cc(ccc1)C(CF)(C)C effect on BACE1 inhibition. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles... | 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 S1(=O)(=O)N(CCCC1)c1cc(cc(NCC)c1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]C1CC1 for BACE1 inhibition. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"S1(=O)... | 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 FC1(F)COC(=NC1(C)c1cc(NC(=O)c2ncc(OC)nc2)ccc1F)N for its BACE1 inhibitory potential. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"FC1(F)COC(=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": "Does the drug Fc1ncccc1-c1cc(ccc1)C1(N=C(N)N(C)C1=O)c1cc(nc(c1)CC)CC inhibit the enzyme BACE1? MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"Fc1ncccc1-c1cc(ccc1... | 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=C(NC(C)C)c1ccc(cc1)-c1n(Cc2nc(N)ccc2)c(cc1)-c1ccccc1 potential as a BACE1 inhibitor. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"O=C(NC(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": "Check if the drug O1CCCCNc2cc(cc(c2)C(=O)NC(Cc2cc1ccc2)C(O)C[NH2+]C(C)(C)c1cc(ccc1)C(C)C)COC inhibits the activity of BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_s... | 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 FCC\\C=C\\c1cc(ccc1)[C@]1(N=C(N)N(C)C1=O)c1ccc(OC(F)F)cc1 likelihood of BACE1 inhibition. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"FCC\\... | 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 Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCC(CC1)C)c1cc(ccc1)C(C)(C)C to inhibit BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_sm... | 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 ability of the drug FC1(F)COC(=NC1(C)c1cc(NC(=O)c2ncccc2)ccc1F)N to inhibit BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"FC1(F)COC(=NC1(C)c1cc(... | 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 Clc1cc(Cl)ccc1C(=O)N[C@@H](Cc1ccc([N+](=O)[O-])cc1)C(=O)N[C@H]([C@@H](O)C[C@H](C(=O)NCC(C)C)C)CC(C)C activity against BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"argu... | 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 drug Fc1c(cccc1OC)-c1cc(ccc1)[C@]1([NH+]=C(N2CC(F)(F)CN=C12)N)c1ccncc1 shows activity against BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smi... | 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 Brc1ccccc1C1C[NH2+]CC1C(=O)N1CCC(CC1c1ccccc1)c1ccccc1 inhibit BACE1? MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"Brc1ccccc1C1C[NH2+]CC1C(=O)N1CCC... | 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 FC1(F)COC(=NC1(C)c1cc(NC(=O)c2ncc(OC(F)(F)F)cc2)ccc1F)N acts as a BACE1 inhibitor. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"FC1(F)COC(... | 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 Clc1ccccc1-c1n(Cc2nc(N)c(OCCO)cc2)c(cc1)-c1ccc(OCCCCC)cc1 is effective at inhibiting BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"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": "Does the drug FC1(F)COC(=NC1(C)c1cc(NC(=O)c2ncc(OC(F)(F)F)cc2)ccc1F)N demonstrate inhibition of BACE1? MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"FC1(F)COC(=... | 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)N(c2cc(cc3n(cc(CC1)c23)CC)C(=O)N[C@H]([C@H](O)C[NH2+]C(CC)C)Cc1ccccc1)C acts as a BACE1 inhibitor. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"dr... | 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 drug's S(=O)(=O)(N(C)c1cc(cc(c1)C(=O)NC(C(O)CC(C(=O)NC(C(C)C)C(=O)NCc1ccccc1)C)COCc1ccccc1)C(=O)NC(C)c1ccccc1)C inhibition 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": "Examine whether the drug Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCC(NC(=O)C)CC1)c1cc(ccc1)C(C)(C)C can inhibit BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\"... | 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 the drug Fc1ncccc1-c1cc(ccc1)[C@]1([NH+]=C(N2CC(N=C12)(C)C)N)c1ccc(OC(F)(F)F)cc1 for its BACE1 inhibitory activity. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smil... | 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 Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CC1)c1oc(cn1)C(C)(C)C can block BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"Fc1cc(cc(F)c1)CC(... | 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 of the drug O(C)c1cccnc1-c1cc2cc(CC(C(=O)NCCC(C)(C)C)C)c(nc2cc1)N as a BACE1 inhibitor. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"O(... | 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 O1c2cc(ccc2OC1)C1(N=C(N)N(C)C1=O)c1cc(ccc1)-c1cccnc1 and O=C(N[C@H]([C@H](O)C[NH2+][C@H]1CC(Cc2nn(cc12)CC(C)(C)C)(C)C)Cc1ccccc1)C capable of inhibiting BACE1? MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_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 S1(=O)(=O)NCC2(N1c1cc(F)ccc1)CC([NH+](CC2)Cc1cc(OC2CCCC2)ccc1)C and Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCC(Nc2ncccc2)CC1)c1cc(ccc1)C(C)(C)C for its 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": "Analyze the effect of the drug Clc1cc(cnc1)-c1cc(ccc1)C1(N=C(C)C(=N1)N)C1CC1 and S1CC(N=C(N)C1)(C)c1cc(ccc1)-c1cc(OC)ccc1 on BACE1 activity. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"argume... | 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 O(C)c1ccc(cc1)C1(N=C(C)C(=N1)N)c1cc(ccc1)-c1cccnc1 and n1cc(ccc1)-c1cc(ccc1)C1(N=C(N2C1=NCCC2)N)c1ccncc1 acts as a BACE1 inhibitor. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_pr... | 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 Clc1ccc(nc1)C(=O)Nc1cc(C2(N=C(N)C3C(C2)C3)C)c(F)cc1 and n1cc(cnc1)-c1cc(ccc1)C1(N=C(C(C)C)C(=N1)N)c1ccccc1 action on BACE1 inhibition. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_predictio... | 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 FC(F)Oc1ccc(cc1)[C@@]1(N=C(N)N(C)C1=O)c1cc(ccc1)CCC and O(CC)c1cc(ccc1OC)C1(N=C(N)N(C)C1=O)c1cc(ccc1)-c1cccnc1 inhibiting BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_... | 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 Fc1cc(cc(F)c1)CC(NC(=O)C(N1CCC(NC(=O)C)(C(CC)C)C1=O)CCc1ccccc1)[C@H](O)[C@H]1[NH2+]Cc2c(C1)cccc2 and S(Oc1cc(ccc1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]C(C(=O)NC1CCCCC1)C)(=O)(=O)Cc1ccccc1 inhibitory effect on BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
... | 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=C1NC(=NC(=C1)CCC)N and S1(=O)(=O)CC(Cc2cc(OC(C)C)c(N)c(F)c2)C(O)C([NH2+]Cc2cc(ccc2)C(C)(C)C)C1 has activity against 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": "Examine if the drug Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCCCC1)c1cc(N2CCOC2=O)ccc1 and S(=O)(=O)(N(C)c1cc(cc(c1)C(=O)NC(C)c1ccc(F)cc1)-c1nc([nH]n1)C([NH3+])(Cc1ccccc1)C)C demonstrates BACE1 inhibition. 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": "Predict if 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+]Cc2cn(nc2)CC(C)(C)C)C1 and Fc1ncccc1-c1cc(ccc1)C1(N=C(N)N(C)C1=O)c1ccncc1 is a potential BACE1 inhibitor. 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": "Can the drug Clc1cc2CC(N=C(NC(Cc3cscc3C#C)C(=O)[O-])c2cc1)(C)C and Clc1cc(ccc1OCCCC)CSC(=[NH2+])N act as a BACE1 inhibitor? MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_sm... | 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 O=C([O-])C(NC1=NC(Cc2c1ccc(c2)C)(C)C)Cc1ccccc1 and O(C)c1cc(ccc1)-c1cc(ccc1)C1(N=C(C)C(=N1)N)C1CCN(CC1)C(=O)C blocks 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": "Analyze the likelihood of the drug S1(=O)(=O)CC(Cc2cc(OC(C)C)c(N)c(F)c2)C(O)C([NH2+]Cc2cc(ccc2)C(C)(C)C)C1 and Clc1ccc(cc1)-c1cc2c(Oc3c(cc(OC)cc3)C23N=C(OC3)N)cc1 inhibiting 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": "Test if the drug Fc1cc(cc(F)c1)CC(NC(=O)c1c2cc(ccc2n(c1)C(=O)N(CCCC)C)C(=O)[O-])C(O)C[NH2+]Cc1cc(OC)ccc1 and Fc1cc(cc(F)c1)CC(NC(=O)c1cc(cc(Oc2ccc(OC)cc2)c1)C(=O)N(CCC)CCC)C(O)C[NH2+]Cc1cc(OC)ccc1 inhibit the BACE1 enzyme. 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": "Assess whether the drug O(CC)c1cc(ccc1OC)C1(N=C(N)N(C)C1=O)c1cc(ccc1)-c1cccnc1 and Fc1c2c(ccc1)C(N=C2N)(c1cc(ccc1)-c1cc(cnc1)C#CC)c1cc(ncc1)C(F)F has an cytostatic effect 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": "Determine the drug's S1(=O)(=O)N(c2cc(cc3c2n(cc3CC)CC1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]C1CCOCC1)CC(F)(F)F and Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1CC2(Oc3ncc(cc13)CC(C)(C)C)CCC2 ability to block 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": "Predict if the drug Oc1ccc(cc1)CC[NH3+] and Fc1ccc(cc1)C[C@H](NC(=O)C)[C@H](O)C[NH2+][C@H]1CC2(Oc3ncc(cc13)CC(C)(C)C)CCC2 demonstrates activity against BACE1. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_pred... | 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... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.