// Retrieve SMILES strings from PubChem database using compound names.
Predict the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of the input molecules.
Predict binding affinity between target protein sequence and small molecule SMILES using Boltz-2.
Predict protein structures with Chai-1 from sequence or FASTA input and return model scoring summaries.
Chroma toolkit skill covering chroma_monomer for single-chain generation, chroma_complex for multi-chain assembly generation, and chroma_symmetry for symmetry-constrained protein design.
Generate new molecules de novo.
[CURRENTLY UNAVAILABLE] DiffDock protein-ligand docking. This tool is not deployed on the current MCP server. Use molclaw-quickvina-docking or molclaw-karmadock-tool as alternatives.
| name | molclaw-compound-retrieve |
| description | Retrieve SMILES strings from PubChem database using compound names. |
| license | MIT license |
| metadata | {"skill-author":"PJLab"} |
Note:
molclaw-file-transfer before execution.molclaw-pdbfixer before execution.molclaw-scp-server to complete tool invocation.The description of tool retrieve_smiles_by_compoundname.
Retrieve SMILES strings from PubChem using compound names.
Args:
compound_names (List[str]): List of input compound names (e.g., ["aspirin", "caffeine"])
Return:
status (str): success/partial_success/error
msg (str): message
retrieve_smiles (List[dict]): List of dict, each containing the keys 'compound_name' and 'smiles'.
--compound_name (str): A compound name of compound_names
--smiles (str): The retrieved SMILES string, if it exists; otherwise, None.
How to use tool retrieve_smiles_by_compoundname :
response = await client.session.call_tool(
"retrieve_smiles_by_compoundname",
arguments={
"compound_names": compound_names
}
)
result = client.parse_result(response)
retrieve_smiles = result["retrieve_smiles"]