molclaw-equiscore-docking

1. EquiScore Docking Ranking Skill

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.

step 1. Retrieve target protein structure (skip if user already provides PDB).

• Use skill molclaw-protein-structure-retrieve.

step 2. Optional chain extraction (only if specific chains are required).

response = await client.session.call_tool(
    "extract_and_save_chains",
    arguments={"pdb_file_path": pdb_path, "chain_ids": chain_ids}
)
result = client.parse_result(response)
pdb_path = result["out_file"]

step 3. Fix receptor structure with PDBFixer.

response = await client.session.call_tool(
    "fix_pdb",
    arguments={
        "input_path": pdb_path,
        "add_hydrogens": True,
        "ph": 7.0,
        "remove_heterogens": True,
        "remove_water": True,
        "replace_nonstandard": True
    }
)
result = client.parse_result(response)
fixed_pdb_path = result["output_file"]

2. EquiScore-based Ranking Flow

step 4. Drug-likeness filtering.

• Keep molecules satisfying: QED >= 0.2 and lipinski_rule_of_5_violations <= 2.
• Always compute from returned result["metrics"]; do not use manually copied values.
• Assert len(metrics) == len(candidate_smiles_list) before filtering.

response = await client.session.call_tool(
    "calculate_mol_drug_chemistry",
    arguments={"smiles_list": candidate_smiles_list}
)
result = client.parse_result(response)

metrics = result["metrics"]

filtered_smiles = [
    m["smiles"] for m in metrics
    if m["qed"] >= 0.2 and m["lipinski_rule_of_5_violations"] <= 2
]

step 5. Build EquiScore docking input.

Important:

• EquiScore needs a docking-result SDF (ligand poses relative to receptor).
• Raw SDF converted directly from SMILES is not sufficient for equiscore_pocket.