ProLIF Docking Pose Analysis Skill
SkillDev toolsProLIF docking-pose analysis skill for batch interaction fingerprints and interaction count summaries.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the ProLIF Docking Pose Analysis Skill skill
What this skill tells your AI
The instructions your AI receives, as published by internscience/molclaw in skills/L1_tools/molclaw-prolif-docking/SKILL.md and read by ahel’s review.
Note:
- Local files are not directly accessible by the server. Please upload them to the server using
molclaw-file-transferbefore execution. - For PDB file inputs, it is recommended to preprocess them using
molclaw-pdbfixerbefore execution. - Please refer to skill
molclaw-scp-serverto complete tool invocation.
[!NOTE] Local files are not directly accessible by the server. Please upload them to the server using
molclaw-file-transferbefore execution. For PDB file inputs, it is recommended to preprocess them usingmolclaw-pdbfixerbefore execution.
Task Description
Batch-analyze multiple docking-generated binding poses and produce interaction fingerprints or interaction count summaries. Use this skill to screen binding modes and evaluate docking result quality.
Routing note: This tool is the primary choice for batch docking fingerprint comparison (≥ 2 poses). For single-structure deep analysis (one pose, one complex), use
molclaw-interaction-visualizerinstead — it provides Schrödinger-style 2D diagrams, PyMOL 3D renderings, and decision-ready JSON that this tool does not produce.
Input Source Mapping
| Parameter | Source Guidance |
|---|---|
protein_path | Generated by structure retrieval/prediction tools (e.g., retrieve_protein_structure_by_*, pred_protein_structure_esmfold, chai1_predict) or a PDB fixed by fix_pdb |
ligand_paths | Generated by docking tools (e.g., molecule_docking_quickvina_fullprocess, hdock_tool, karmadock_tool) as pose files (.sdf/.mol2/.pdbqt) |
ligand_format | Must match the upstream docking output format: sdf, mol2, or pdbqt |
template_smiles | Required when ligand_format is pdbqt to provide ligand chemistry reference |
Usage
Tool: prolif_docking
Summarize docking poses with ProLIF and return a CSV of interaction fingerprints plus summary metrics.
Args:
protein_path (str): Path to the receptor protein structure.
ligand_paths (List[str]): List of ligand pose files.
ligand_format (str): Ligand format identifier (e.g., 'sdf', 'mol2', 'pdbqt').
template_smiles (str|None): Optional template SMILES; required when ligand_format is 'pdbqt'.
interactions (List[str]|None): Optional interaction types to compute.
count (bool): If True, compute interaction counts instead of fingerprints. Default: False.
vicinity_cutoff (float|None): Optional distance cutoff for vicinity interactions.
params_json (str|None): Optional JSON parameter file path for ProLIF interaction settings.
Return:
status (str): 'success' or 'error'.
msg (str): Human-readable summary or error message.
command (str): The executed command label ('docking').
output_dir (str|None): Run-specific directory under tool_result/prolif_result.
output_file (str|None): Path to the produced CSV summary file.
n_frames (int|None): Number of processed frames where applicable.
n_interactions (int|None): Number of interaction columns in output.
frequent_interactions (List[dict]|None): High-frequency interactions (>30%) with keys 'interaction' and 'frequency'.
result_summary (dict|None): Full summary dictionary from the wrapper.
How To Use prolif_docking
response = await client.session.call_tool(
"prolif_docking",
arguments={
"protein_path": "relative/path/to/receptor.pdb",
"ligand_paths": [
"relative/path/to/pose1.sdf",
"relative/path/to/pose2.sdf"
],
"ligand_format": "sdf",
"interactions": ["Hydrophobic", "HBDonor"]
}
)
result = client.parse_result(response)
key_output = result["output_file"]
Example Parameter Sets
# 1) Main mode
{
"protein_path": "relative/path/to/receptor.pdb",
"ligand_paths": [
"relative/path/to/docking_poses.sdf"
],
"ligand_format": "sdf"
}
# 2) Variant mode
{
"protein_path": "relative/path/to/receptor.pdb",
"ligand_paths": [
"relative/path/to/pose1.pdbqt",
"relative/path/to/pose2.pdbqt"
],
"ligand_format": "pdbqt",
"template_smiles": "CCO",
"count": True,
"vicinity_cutoff": 4.0
}
Signals
- GitHub stars
- 33
- Forks
- 3
- Last commit
- Aug 2026
Advanced
- Catalog kind
- skill
- Gateway key
molclaw-prolif-docking- Source
- github.com/internscience/molclaw