Molclaw Equiscore Tool

EquiScore Multi-Tool Workflow

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.

> [!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.

Usage

1. Pocket Extraction

The description of tool equiscore_pocket.

Extract binding pockets from docking results and prepare split single-molecule SDFs for EquiScore screening.
Args:
    docking_result (str): Path to a docking-result SDF file.
    receptor_pdb (str): Path to the receptor PDB file.
    pocket_cutoff (float|None): Optional numeric cutoff for pocket detection.
    dry_run (bool|None): If True, validate inputs and prepare outputs without executing EquiScore.
Return:
    status (str): 'success' or 'error'.
    msg (str): Human-readable summary or error message.
    command (str): The subcommand executed ('get_pocket').
    run_dir (str|None): Run-specific directory under tool_result/equiscore_result.
    single_sdf_dir (str|None): Path to directory containing split single-molecule SDFs.
    pocket_dir (str|None): Path to the generated pocket folder.
    split_sdf_count (int|None): Number of split SDF files created.
    pocket_item_count (int|None): Number of pocket entries generated.
    sample_single_sdfs (List[str]|None): Sample single-SDF filenames.
    sample_pockets (List[str]|None): Sample pocket directory names.

How to use tool equiscore_pocket :

response = await client.session.call_tool(
    "equiscore_pocket",
    arguments={
        "docking_result": "relative/path/to/docking_result.sdf",
        "receptor_pdb": "relative/path/to/receptor.pdb",
        "pocket_cutoff": 10.0,
        "dry_run": True
    }
)
result = client.parse_result(response)
key_output = result["single_sdf_dir"]

Example parameter sets

# 1) Main mode
{
    "docking_result": "relative/path/to/docking_result.sdf",
    "receptor_pdb": "relative/path/to/receptor.pdb",
    "pocket_cutoff": None,
    "dry_run": False
}

# 2) Variant mode
{
    "docking_result": "relative/path/to/docking_result.sdf",
    "receptor_pdb": "relative/path/to/receptor.pdb",
    "pocket_cutoff": 8.5,
    "dry_run": True
}

2. Pocket Screening

The description of tool equiscore_screen.

Score a pocket library with EquiScore and return prediction CSV plus summary statistics.
Args:
    pocket_dir (str): Path to a pocket directory produced by `equiscore_pocket`.
    ngpu (int): Number of GPUs to use. Default: 1.
    batch_size (int): Inference batch size. Default: 128.
    num_workers (int): Number of worker processes for data loading. Default: 8.
    weight_path (str|None): Optional path to model weights.
    multi_pose (bool): If True, score multiple poses per ligand.
    pose_num (int): Number of poses to evaluate when `multi_pose` is True. Default: 1.
    debug (bool): Enable debug mode.
    dry_run (bool|None): If True, validate inputs without running EquiScore.
Return:
    status (str): 'success' or 'error'.
    msg (str): Human-readable summary or error message.
    command (str): The subcommand executed ('screen').
    run_dir (str|None): Run-specific directory under tool_result/equiscore_result.
    output_dir (str|None): Directory where screening outputs were written.
    predictions_path (str|None): Path to the CSV file with raw predictions.
    prediction_count (int|None): Number of prediction rows in the CSV.
    score_field (str|None): CSV column used for scoring, if detected.
    max_score (float|None): Maximum observed score.
    min_score (float|None): Minimum observed score.
    mean_score (float|None): Mean score.
    median_score (float|None): Median score.

Scoring Interpretation (EquiScore)

• EquiScore is trained as a classifier (active=1, decoy=0), so 0.5 can be used as a rough reference boundary.
• In practical virtual screening, absolute thresholding is less robust than ranking.
• Recommended usage:
• Sort predictions by score column (commonly test_pred) in descending order.
• Select top N or top N% compounds for downstream validation.
• Typical settings include top 1% for enrichment-style filtering or top 50-200 molecules for follow-up.
• For higher confidence, combine EquiScore ranking with another docking/scoring method for consensus prioritization.

How to use tool equiscore_screen :

response = await client.session.call_tool(
    "equiscore_screen",
    arguments={
        "pocket_dir": "relative/path/to/pockets",
        "ngpu": 1,
        "batch_size": 128,
        "num_workers": 8,
        "multi_pose": False,
        "pose_num": 1,
        "debug": False,
        "dry_run": False
    }
)
result = client.parse_result(response)
key_output = result["predictions_path"]

Example parameter sets

# 1) Main mode
{
    "pocket_dir": "relative/path/to/pockets",
    "ngpu": 1,
    "batch_size": 128,
    "num_workers": 8,
    "multi_pose": False,
    "pose_num": 1,
    "debug": False,
    "dry_run": False
}

# 2) Variant mode
{
    "pocket_dir": "relative/path/to/pockets",
    "ngpu": 2,
    "weight_path": "relative/path/to/custom_equiscore.pt",
    "multi_pose": True,
    "pose_num": 5,
    "debug": False,
    "dry_run": False
}

3. End-to-End Pipeline

The description of tool equiscore_pipeline.

Run one-click EquiScore workflow for pocket extraction and screening from docking output.
Args:
    docking_result (str): Path to a docking-result SDF file.
    receptor_pdb (str): Path to receptor PDB file.
    ngpu (int): Number of GPUs for the screening stage. Default: 1.
    weight_path (str|None): Optional path to a custom EquiScore model checkpoint.
    multi_pose (bool): Enable multi-pose scoring mode.
    pose_num (int): Number of poses to evaluate when `multi_pose` is True. Default: 1.
    dry_run (bool|None): Validate inputs and print command flow without launching EquiScore.
Return:
    status (str): 'success' or 'error'.
    msg (str): Human-readable summary or error message.
    command (str): The subcommand executed ('pipeline').
    run_dir (str|None): Run-specific directory under tool_result/equiscore_result.
    work_dir (str|None): Pipeline working directory holding intermediate files.
    single_sdf_dir (str|None): Directory containing the split single-molecule SDFs.
    pocket_dir (str|None): Directory containing extracted pocket data.
    predictions_path (str|None): Path to the final EquiScore prediction CSV.
    split_sdf_count (int|None): Number of split SDF files produced.
    pocket_item_count (int|None): Number of pocket entries generated during extraction.
    prediction_count (int|None): Number of rows in the prediction CSV.
    score_field (str|None): CSV column used as the score.
    max_score (float|None): Maximum score.
    min_score (float|None): Minimum score.
    mean_score (float|None): Mean score.
    median_score (float|None): Median score.

How to use tool equiscore_pipeline :

response = await client.session.call_tool(
    "equiscore_pipeline",
    arguments={
        "docking_result": "relative/path/to/docking_result.sdf",
        "receptor_pdb": "relative/path/to/receptor.pdb",
        "ngpu": 1,
        "multi_pose": False,
        "pose_num": 1,
        "dry_run": False
    }
)
result = client.parse_result(response)
key_output = result["predictions_path"]

Example parameter sets

# 1) Main mode
{
    "docking_result": "relative/path/to/docking_result.sdf",
    "receptor_pdb": "relative/path/to/receptor.pdb",
    "ngpu": 1,
    "multi_pose": False,
    "pose_num": 1,
    "dry_run": False
}

# 2) Variant mode
{
    "docking_result": "relative/path/to/docking_result.sdf",
    "receptor_pdb": "relative/path/to/receptor.pdb",
    "ngpu": 2,
    "weight_path": "relative/path/to/custom_equiscore.pt",
    "multi_pose": True,
    "pose_num": 5,
    "dry_run": False
}

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: InternScience
• Source: InternScience/MolClaw
• License: MIT

Install and usage instructions live in the source repository linked above.