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npx skills add https://github.com/InternScience/MolClaw --skill molclaw-boltz2-affinity命令会保持在同一行。复制前请横向滚动并检查完整内容。
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| name | molclaw-boltz2-affinity |
| description | Predict binding affinity between target protein sequence and small molecule SMILES using Boltz-2. |
| license | MIT license |
| metadata | {"skill-author":"PJLab"} |
Note:
molclaw-file-transfer before execution.molclaw-pdbfixer before execution.molclaw-scp-server to complete tool invocation.step 1. Use skill molclaw-protein-sequence-retrieve to get the target protein sequence information. If the target protein sequence has been provided, skip this step.
step 2. Finally use tool pred_binding_affinity_boltz2 to predict the binding affinity.
Tool description:
Use Boltz to predict binding affinity between protein (receptor) and small molecule (ligand).
The server selects the output directory. This tool is for small-molecule ligands, not peptide/protein partners; ligands exceeding the Boltz affinity atom limit are returned as a structured model-capability error rather than a timeout.
Args:
protein (List[dict]): Protein chains, each element contains 'chain' and 'sequence' (e.g., [{{'chain': 'A', 'sequence': 'MGNAAAAKKGSEQASQRRSSLEQP*'}}])
smiles (str): Input SMILES string (e.g., "N[C@@H](Cc1ccc(O)cc1)C(=O)O")
Return:
status (str): success/error
msg (str): message
affinity_probability_binary (float): Represents the predicted probability (ranging from 0 to 1) that a ligand is a binder, making it ideal for distinguishing active compounds from decoys during the hit-discovery stage. A value below 0.5 indicates uncertain or weak binding.
affinity_pred_value (float): Estimates the specific binding affinity as log10(IC50) in μM to quantify how small molecular modifications affect potency, serving as a key metric for ligand optimization phases like hit-to-lead and lead-optimization.
complex_cif_file (str): Structure file of the protein–molecule complex
Tool usage:
response = await client.session.call_tool(
"pred_binding_affinity_boltz2",
arguments={
"protein": protein_chains,
"smiles": smiles
}
)
result = client.parse_result(response)
affinity_probability_binary = result["affinity_probability_binary"]
affinity_pred_value = result["affinity_pred_value"]
Current capability boundary: Boltz affinity rejects ligands with more than 128 atoms. For peptide ligands such as PTHrP/TIP39 fragments, this is expected behavior; use protein-peptide structure/docking workflows such as Chai-1/HDOCK plus interaction_visualizer(mode="peptide") instead of interpreting the Boltz rejection as a server failure.
All tools utilized within MolClaw skills connect via the MCP protocol. This skill is the unified guide for connecting to the deployed MCP server before invoking tools.
Formats extracted execution patterns into standard MolClaw skill documents. Accepts structured input from the Skill Crystallization Meta-Workflow (L2-12) and outputs a properly formatted L1 or L2 skill document conforming to MolClaw conventions. This skill ensures that auto-generated skills are structurally identical to expert-curated skills, enabling seamless integration into the skill matching and loading pipeline.
Predict the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of the input molecules.
基于 SOC 职业分类