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Generate new molecules sampling from the input molecule.
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Based on SOC occupation classification
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.
Retrieve SMILES strings from PubChem database using compound names.
Generate new molecules de novo.
| name | molclaw-mol2mol-sampling |
| description | Generate new molecules sampling from the input molecule. |
| 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 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): Minimum similarity threshold, default is 0.6
prior_type (str): Prior type for generation, options: ['scaffold_generic', 'scaffold', 'mmp', 'similarity', 'high_similarity', 'medium_similarity'], default is 'similarity'
lipinski (bool): Whether to apply Lipinski's rule of five filtering, default is True
filter_preset (str): Filter preset, options: ['none', 'minimal', 'default', 'strict'], default is '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_denovo_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"]