| name | molclaw-drug-likeness |
| description | Compute the drug-likeness metrics (QED score and Number of violations of Lipinski's Rule of Five) of the input candidate molecules (SMILES format). |
| license | MIT license |
| metadata | {"skill-author":"PJLab"} |
Molecular Drug-likeness Metrics Calculation
Note:
- Local files are not directly accessible by the server. Please upload them to the server using
molclaw-file-transfer before execution.
- For PDB file inputs, it is recommended to preprocess them using
molclaw-pdbfixer before execution.
- Please refer to skill
molclaw-scp-server to complete tool invocation.
The description of tool calculate_mol_drug_chemistry.
Compute key drug-likeness metrics for each SMILES.
Args:
smiles_list (List[str]): List of input SMILES strings, (e.g., ["N[C@@H](Cc1ccc(O)cc1)C(=O)O", "CC(C)C1=CC=CC=C1"])
Return:
status (str): success/error
msg (str): message
metrics (List[dict]): List of dict, each containing feature keys.
--smiles (str): A SMILES string of smiles_list
--qed (float): Quantitative Estimate of Drug-likeness (QED) score
--lipinski_rule_of_5_violations (int): Number of violations of Lipinski's Rule of Five
How to use tool calculate_mol_drug_chemistry :
response = await client.session.call_tool(
"calculate_mol_drug_chemistry",
arguments={
"smiles_list": smiles_list
}
)
result = client.parse_result(response)
druglikeness_metrics = result["metrics"]
