| name | molclaw-mol2mol-sampling |
| description | Generate new molecules sampling from the input molecule. |
| license | MIT license |
| metadata | {"skill-author":"PJLab"} |
Mol2Mol Molecule Generation
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 reinvent_mol2mol_sampling.
Generate new molecules sampling from the input molecule using different priors ('similarity': broad exploration, 'medium_similarity': balanced exploration, 'high_similarity': conservative optimization, 'scaffold': strict scaffold preservation, 'scaffold_generic': generic scaffold preservation, 'mmp': MMP-style local modifications).
Args:
smiles (str): Input SMILES string
n (int): Number of molecules for sampling
min_similarity (float): Required minimum similarity threshold (commonly 0.6)
prior_type (str): Required prior type; options: ['scaffold_generic', 'scaffold', 'mmp', 'similarity', 'high_similarity', 'medium_similarity'] (commonly 'similarity')
lipinski (bool): Required flag controlling Lipinski filtering (commonly True)
filter_preset (str): Required filter preset; options: ['none', 'minimal', 'default', 'strict'] (commonly 'default')
Return:
status (str): success/error
msg (str): message
save_smiles_file (str): Path to the saved SMILES file
output_smiles_list (List[str]): List of generated SMILES strings
How to use tool reinvent_mol2mol_sampling:
response = await client.session.call_tool(
"reinvent_mol2mol_sampling",
arguments={
"smiles": smiles,
"n": n,
"min_similarity": min_similarity,
"prior_type": prior_type,
"lipinski": True,
"filter_preset": filter_type
}
)
result = client.parse_result(response)
output_smiles_list = result["output_smiles_list"]