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molclaw-denovo-sampling
Generate new molecules de novo.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
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Generate new molecules de novo.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
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.
[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-denovo-sampling |
| description | Generate new molecules de novo. |
| 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_denovo_sampling.
Generate new molecules de novo.
Args:
n (int): Number of molecules for sampling
lipinski (bool): Whether to apply Lipinski's rule of five filtering, default is True
filter_preset (str): Filter preset, options: ['none', 'minimal', 'default', 'strict', 'druglike', 'all'], default is 'druglike'
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_denovo_sampling",
arguments={
"n": n,
"lipinski": True,
"filter_preset": filter_type
}
)
result = client.parse_result(response)
output_smiles_list = result["output_smiles_list"]