conversations listlengths 4 8 | system stringclasses 1
value | tools stringclasses 14
values |
|---|---|---|
[
{
"from": "human",
"value": "How likely is the drug COc1ccc2c(Oc3ccc(CC(=O)Nc4cn(C)nc4C)c(OC)c3)ccnc2c1 to exhibit lipophilic behavior? MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"COc1cc... | 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 COc1cc(ccc1Nc2ncc3N(C)C(=O)CCN(C4CCCCC4)c3n2)C(=O)NC5CCN(C)CC5 compared to other substances? 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": "How does the drug CN1CCC(CC1)NC(=O)c2ccc(Oc3ccc(cc3)C#C[C@]4(O)CN5CCC4CC5)cc2 behave in nonpolar environments? MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CN... | 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 Clc1ccc(CNc2nc(nc3ncccc23)N4CCN(CC4)C(=O)[C@H]5CCCCN5)c(Cl)c1 behave in fat-based solvents? MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Clc... | 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 CC(=O)C1=CN(Cc2ccccc2)C(=O)N(Cc3ccc(F)cc3)C1=O? MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC(=O)C1=CN(Cc2cccc... | 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 COc1ccccc1CNCCc2cccc(CCNC[C@H](O)c3ccc(O)c4NC(=O)Sc34)c2 in lipophilic environments? 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": "Predict how lipophilic the compound CCN(CC)C(=O)c1ccc(cc1)C(=C2CCN(Cc3ccc(F)cc3)CC2)c4ccccn4 is. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCN(CC)C(=O)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": "How lipophilic is the drug CC1=CN([C@H]2CCCN(C2)S(=O)(=O)c3ccc(O)c(Oc4cc(F)cc(Cl)c4)c3)C(=O)NC1=O based on its chemical structure? 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": "What is the drug CCN(C1CCN(CCC(c2ccc(F)cc2)c3ccc(F)cc3)CC1)C(=O)Cc4ccc(cc4)S(=O)(=O)C's potential to be absorbed into lipid environments? 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": "Estimate the drug OCCNCc1cc(Br)ccc1OCc2ccc(Cl)cc2's affinity for lipid tissues or areas. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"OCCNCc1cc(Br)ccc1OCc2ccc... | 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 COc1ccc(Cl)cc1C(=O)NCCc2ccc(cc2)S(=O)(=O)NC(=O)NC3CCCCC3 interact with nonpolar solvents or lipids? 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": "Can you assess the lipophilicity of the molecule NC1(CCC1)c2ccc(cc2)c3ncc4cccn4c3c5ccccc5? MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"NC1(CCC1)c2ccc(cc2)c3n... | 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 OC[C@H](O)C(=O)N1CCC(=CC1)c2ccc(cc2F)N3C[C@H](COc4ccon4)OC3=O's solubility in lipophilic environments? 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": "How well does the drug NC(=O)c1ccc(O[C@H]2C[C@H]3CC[C@@H](C2)N3Cc4ccccc4)cn1 and C[C@@H]1CN(CCN1c2ncc(OCc3ccncc3C#N)cn2)c4noc(n4)C5CC5 dissolve in lipid environments? MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_prope... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "How well does the drug CCc1nc(N)nc(N)c1c2ccc(Cl)cc2 and COc1cc(OC)c(cc1NC(=O)C)S(=O)(=O)N2C(C)Cc3ccccc23 dissolve in nonpolar or lipid-rich solutions? 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": "What is the molecule CN(C)CC(O)COc1ccc(Nc2cc(ncn2)N(CCC#N)c3cc(Cl)ccc3Cl)cc1 and C[C@H](CO)Nc1nc(SCc2cccc(F)c2F)nc3NC(=O)C=Nc13's tendency to be soluble in lipids? 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": "How lipophilic is the drug Cn1cc(cc1c2c3C(=O)N(CC#CCn4cncn4)C(=O)N(CC5CC5)c3nn2Cc6ccnc7ccc(Cl)cc67)C#N and CC(C)C(NC(=O)CN1C(=O)C(=CC=C1c2ccccc2)NC(=O)NCc3ccncc3)C(=O)C(F)(F)F in a biological system? MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Determine how the drug COc1cc2c(Nc3ccc(Cl)cc3F)ncnc2cc1OCC4CCNCC4 and Clc1ccc(cc1C(=O)NCC2CCCCC2)N3N=CC(=O)NC3=O interacts with nonpolar solvents. 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": "What is the extent of the drug OC(=O)c1ccccc1C2=C3C=CC(=O)C=C3Oc4cc(O)ccc24 and Oc1cc(O)c2C(=O)C(=COc2c1)c3ccc4OCOc4c3's lipophilic behavior? MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"argum... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "What is the drug CC(C)N1CCC[C@H](CN2C(=Nc3ccc(cc3C2=O)c4ccc(Cl)cc4)C)C1 and CC(=O)NCc1ccc(CN2CCN(CC2)c3ccc(F)cc3F)cc1's solubility in fat or lipid environments? 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": "Estimate the drug Clc1cccc(CN2C=CN3C(=O)C=C(N=C23)N4CCOCC4)c1Cl and COc1cc(O)ccc1\\C=C\\2/CCCN=C2c3cccnc3's ability to dissolve in hydrophobic solvents. 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": "Provide an estimate of the compound OC1CCN(CC1)C(=O)c2ccc(cc2)C(=C3CCN(Cc4cscn4)CC3)c5cccc6cccnc56 and O=C1COc2ccccc2N1CCN3CCC(CC3)NCc4cc5OCCOc5cn4's lipophilicity. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_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": "Can you estimate the compound COc1cc(ccc1Nc2ncc(Cl)c(n2)c3cnc4cc(F)ccn34)N5CCN(CC5)C(=O)C and CC(C)n1c(C)ncc1c2nc(Nc3ccc(cc3)C(=O)N4CCCN(C)CC4)ncc2F's lipophilicity? MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_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": "Estimate the partitioning of the drug O=C(NC1(CC1)C#N)[C@@H]2CCCC[C@H]2C(=O)N3CCN(CC3)c4nc5ncccc5s4 and C[C@]12N[C@H](Cc3ccccc13)c4ccccc24 in lipid and aqueous environments. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"dru... | 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 C[C@H](CO)Nc1nc(SCc2cccc(F)c2F)nc3[nH]c(N)nc13 and CNC(=O)C1(CCN(CC[C@H](CN(C)C(=O)c2cc(cc3ccccc23)C#N)c4ccc(Cl)c(Cl)c4)CC1)N5CCCCC5=O's lipophilicity based on its chemical structure. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Determine the affinity of the drug CCNC(=O)c1cc2c(c(cnc2[nH]1)c3cncc(c3)C(=O)NC)n4ccc(n4)C(F)(F)F and CCCC(=O)Nc1ccc(OCC(O)CNC(C)C)c(c1)C(=O)C for lipid-based media. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_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": "What is the lipophilicity of the compound COc1cc(ccc1Nc2ncc(Cl)c(n2)c3cnc4cc(Cl)ccn34)N5CCN(CC5)C(=O)C and CC(C)CCOc1ccc(N)cc1? MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"dru... | 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 lipophilicity of the drug NC(=O)C1CCN(CC(=O)Nc2ccc3OCOc3c2)CC1 and CCN(CC)C(=O)c1ccc(cc1)C(=C2CCNCC2)c3ccccc3 based on its molecular properties? MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_predic... | 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 much of the drug CN(C)c1ccc(cc1)S(=O)(=O)NC(=O)N2CCC(CC2)N3CCC(CC3)Oc4ccc(Cl)c(C)c4Cl and OC(=O)[C@H](Cc1ccc(F)cc1)N2CCC(CN3CCC(CC3)Oc4ccc(Cl)c(Cl)c4)CC2 will dissolve in lipophilic conditions? MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"v... | 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 Nc1ccc2c(c1)C(=O)C(=O)c3cc(N)ccc23 and COc1ccc(cc1)c2nc(nc(O)c2C#N)N3CCOCC3's solubility in lipid environments. 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": "How does the drug CCCSc1ncccc1C(=O)N[C@@H]2[C@@H]3CC4C[C@H]2C[C@@](O)(C4)C3 and CC1=CC2=NNC(=O)N2c3cc(ccc13)c4cn[nH]c4 behave in lipid-dominant systems? 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": "How much does the drug Nc1ccc2nc(sc2c1)c3ccc(O)cc3 and CCN(CCCOCCOCCc1ccccc1)CCc2ccc(O)c3nc(O)sc23 dissolve in lipid phases? 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": "Can you estimate how the drug CCCc1c(cnn1c2ccccc2)C(=O)Nc3cc(C)on3 and CNc1nc(C)c(s1)c2nc(Nc3cccc(c3)N4CCNCC4)ncc2C#N interacts with lipophilic solvents? 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": "Can you predict how the drug O=C1C=C(N=C2N(Cc3cccc4ccccc34)C=CN12)N5CCOCC5 and CC(C)C(NC(=O)CN1C(=O)C(=CC=C1c2ccccc2)NC(=O)Cc3cccc(c3)C(=O)O)C(=O)C(F)(F)F will interact with lipid molecules? 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": "What is the degree of lipid solubility of the drug COc1ncc2N=CC(=O)N(CCN3CCC(CC3)NCc4cc5OCCOc5cn4)c2n1 and CCOC(=O)[C@H](Cc1ccc(cc1)[N+](=O)[O-])NC(=O)c2ccccc2? 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": "How does the drug CC(C)n1c(C)ncc1c2ccnc(Nc3ccccc3)n2 and COc1cc(Nc2nc(N[C@@H](C)c3ncc(F)cn3)nc(N4CCOCC4)c2Cl)n[nH]1 partition between water and lipophilic phases? 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": "Predict the compound COc1cc(ccc1Nc2ncc(Br)c(n2)c3cnc4ccccn34)N5CCN(CC5)C(=O)C and COc1cc2ncnc(Nc3cccc(Cl)c3F)c2cc1CN(C)C4(CCN(C)CC4)C(=O)N's affinity for lipids. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_p... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Estimate the compound Oc1c2C(=O)NNC(=O)c2nc3cc(Cl)cc(Cl)c13 and CN1CCN(CC1)C2=Nc3cc(Cl)ccc3Nc4ccccc24's lipid solubility. 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": "How well does the drug Cc1ncc([N+](=O)[O-])n1CCO and COc1cc(ccc1Nc2ncc(Cl)c(n2)c3cnc4c(F)cccn34)N5CCN(CC5)C(=O)C dissolve in fats or oils? MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"argument... | 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 CCN(c1ccnc(Nc2cc(cc(c2)N3CCOCC3)N4CCOCC4)n1)c5cc(CO)ccc5C and CN1C(=O)CC(C)(CCc2cccc(c2)c3ccccc3)N=C1N's solubility in nonpolar solvents? MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_predicti... | 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 would you characterize the drug C[C@@H](CO)Oc1cc(Oc2ccc(cc2)S(=O)(=O)C)cc(c1)C(=O)Nc3nccs3 and CC(=O)Nc1ccc2ccn(c3cc(Nc4ccn(C)n4)n5ncc(C#N)c5n3)c2c1's lipophilicity? MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_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": "How much affinity does the drug Cc1ccc(cc1NC(=O)c2ccc(OCc3ccccn3)cc2)c4cc(n[nH]4)C(F)(F)F and CC(C)n1c(C)ncc1c2ccnc(Nc3ccc(cc3)N4CCN(CC4)C(=O)CO)n2 have for lipid environments? 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": "Measure the lipophilicity of the compound C[C@H]1CCCN1CCc2oc3ccc(cc3c2)c4ccc(cc4)C#N and Cc1cccc2c3CN(CCc3[nH]c12)C(=O)[C@@H]4CCCC[C@H]4C(=O)NC5(CC5)C#N. 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": "Provide an estimate of the drug NC1CCc2cc(OC(=O)c3ccccc3)c(OC(=O)c4ccccc4)cc2C1 and CC(=O)N[C@@H]1CC2(CCN(Cc3ccc4[nH]c5ccccc5c4c3)CC2)c6ccccc16's ability to dissolve in oils. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"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": "How much will the drug COc1cc(F)ccc1c2cncc(CNC(=O)C3CCC3)c2 and OC1(C(=O)Nc2ccccc12)c3ccccc3 partition into lipid layers? 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": "How does the molecule CNC(=O)c1ccccc1Sc2ccc3c(\\C=C\\c4ccccn4)n[nH]c3c2 and NC(=O)c1c(N)sc2CCCCc12's molecular structure influence its lipophilicity? 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": "What is the drug CN[C@@H](C)C(=O)N[C@H](C(=O)N[C@H]1CCCN(CCc2ccc(cc2)C(F)(F)F)C1)C(C)(C)C and COc1cc2N(CCN3CCC(CC3)NCc4cc5OCCOc5cn4)C(=O)C=Cc2cn1's affinity for nonpolar solvents? 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 COCCCc1cc(Nc2ccnc(NCc3onc(C)c3)n2)n[nH]1 and O=C(NC1(CC1)C#N)[C@@H]2CCCC[C@H]2C(=O)N3CCN(CC3)c4nc(cs4)c5ccccc5's solubility in oil or fat-based solvents. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug... | 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 Clc1ccc2nc(ccc2c1C(=O)NCC34CC5CC(CC(C5)C3)C4)N6CCC[C@@H](C6)NCCc7nnn[nH]7 and CCN1CCC[C@H](C1)C(=O)N2CCN(CC2)C(=O)Nc3ccc(Cl)c(Cl)c3's affinity for lipid-based environments. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": ... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "Assess the drug CC(C)NC[C@@H](C(=O)N1CCN(CC1)c2ncnc3[C@H](O)C[C@@H](C)c23)c4ccc(Cl)cc4 and Cc1ccc(CN2[C@@H]3CC[C@H]2C[C@H](C3)Oc4cccc(c4)C(=O)N)s1's solubility in lipid-rich media. 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": "How lipophilic is the compound Cc1ccsc1C(=CCCN2CCC[C@H](C2)C(=O)O)c3sccc3C and CCC(C)(C)C(=O)O[C@H]1C[C@@H](C)C=C2C=C[C@H](C)[C@H](CC[C@@H](O)C[C@@H](O)CC(=O)O)[C@@H]12? MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_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": "Estimate the lipophilicity of the drug O=C(CCc1ccccc1)c2ccccc2 and CCCN(CCC)S(=O)(=O)c1ccc(cc1)C(=O)O. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"O=C(CCc1cc... | 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 [O-][n+]1cc(ccc1Cl)C(=O)Nc2ccc(F)cc2 and CN(C1CCN(Cc2ccc(nc2)C(F)(F)F)CC1)C(=O)Cc3ccc(cc3)n4cnnn4's behavior in a lipid-dominant system. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_predicti... | 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 lipophilicity of the compound COc1ccc(cc1)c2cc(O)c3cc(Cl)ccc3n2 and Nc1ncc(nc1C(=O)Nc2cccnc2)c3ccc(cc3)S(=O)(=O)N4CCOCC4. 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": "How well does the drug O=C(NCc1ccncc1)c2ccc(Oc3ccccc3C#N)cc2 and CCCC(=O)Nc1ccc(Cl)c(c1)c2oc3cccnc3n2 dissolve in nonpolar solvents? 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": "What is the lipophilic tendency of the drug CO[C@@H]1CC[C@@]2(CC1)Cc3ccc(cc3C24N=C(C)C(=N4)N)c5cc(Cl)cc(c5)C#N and O[C@@H](CNCCCSCCOCCc1ccccc1)c2ccc(O)c3NC(=O)Sc23? MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_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": "How would the molecule NC(=O)C[S+]([O-])C(c1ccccc1)c2ccccc2 and CC(C)NC[C@H](O)COc1cccc2ccccc12 dissolve in a lipid-rich environment? 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": "What is the degree of lipophilicity of the compound COc1ccccc1CN(C(=O)C)c2cnccc2Oc3ccccc3 and COc1ccccc1C(=O)NCC23CC4CC(CC(C4)C2)C3? 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": "Estimate the lipophilicity profile of the molecule CN(C1CCN(C)CC1)c2nccc(Nc3cc(NC(=O)c4ccnc(c4)N5CCOCC5)ccc3C)n2 and COc1cc2c(Nc3ncc(CC(=O)Nc4cccc(F)c4)s3)ncnc2cc1OCCCN5CCC(CO)CC5. 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 how the drug NC1=CC2=NNC(=O)N2c3cc(ccc13)c4ccsc4 and CCCSc1ncccc1C(=O)NC2CCCCC2 interacts with lipophilic solvents. MemoryPool(arguments=dict_keys([]))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_... | 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 lipophilic nature of the drug C[C@H]1CN(Cc2cc(Cl)ccc2OCC(=O)O)CCN1C(=O)Cc3ccc(Cl)cc3 and Fc1ccc(cc1F)C(=O)Nc2ccc(Cl)nc2 in lipid solutions? 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": "How does the drug NCCCC(=O)OCCN1CCN(CCCN2c3ccccc3Sc4ccc(Cl)cc24)CC1 and Cc1ccc(NC(=O)CCC2CCCCC2)cc1NC(=O)c3ccc(O)cc3 behave in lipid environments? 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": "How easily does the molecule OC1=CC=Cc2cc(O)c(O)c(O)c2C1=O and COc1ccc(CNC(=O)N(CC=C)C2CCN(CCC(c3ccccc3)c4ccccc4)CC2)cc1 dissolve in lipophilic solvents? 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": "How lipophilic is the drug on a scale from low to high? 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 can the drug's lipophilicity be predicted? 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 hydrophobic systems? 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": "Forecast the lipophilicity of the compound. 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": "Estimate the drug's solubility in oil-based 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 the solubility of the drug in lipid-rich 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": "What is the drug's likelihood of being soluble in fats? 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 likely is the molecule to partition into lipophilic areas? 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": "Predict the molecule's potential for lipid dissolution. 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 lipid phases? 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 drug's solubility in lipid and nonpolar solvents? 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": "Calculate 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": "Determine 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": "Assess how well the molecule mixes with lipids. 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": "Estimate the drug's distribution in lipid-based systems. 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": "Evaluate how lipophilic the molecule 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 does the drug's polarity affect its lipophilicity? 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 estimate the lipid solubility 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": "How does the compound behave in terms of lipophilicity? 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 the drug would behave in fat-rich environments. 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 tendency to interact with fatty substances? 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": "How strongly does the drug interact with lipid layers? 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": "Calculate the lipophilicity for the compound. 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 much of the drug will dissolve in lipophilic 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": "Predict the molecule's behavior in lipophilic conditions. 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 drug's affinity for lipid phases in biological systems? 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": "Assess the lipophilicity of the compound. 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": "Determine the lipophilicity of the compound. 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": "Determine the compound's affinity for lipophilic 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": "How well does the drug partition into lipid phases? 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": "Estimate the drug's lipid affinity. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
},
{
"fro... | 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 drug to accumulate in lipophilic 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": "Assess the molecule's lipid affinity. MemoryPool(arguments=dict_keys(['user_smiles']))"
},
{
"from": "function_call",
"value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
},
{
"f... | You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments ... | [{"name": "drug_property_prediction", "description": "Predict properties of drugs based on SMILES", "parameters": {"type": "object", "properties": {"drug_smiles": {"type": "array", "items": {"type": "string"}, "description": "Drug smile"}, "property_name": {"type": "string", "description": "Task name, actually reffered... |
[
{
"from": "human",
"value": "How strong is the compound's lipophilicity? 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 well does the drug interact with nonpolar substances? 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": "Estimate the drug's tendency to dissolve in fat or oils. 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 drug's behavior in lipid systems? 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... |
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