Agent Skills
› synthetic-sciences/openscience
› molecular-docking
molecular-docking
GitHub提供端到端分子对接工作流，涵盖靶点与配体准备、口袋检测、Vina/DiffDock对接、打分及相互作用分析。适用于基于结构的药物设计、虚拟筛选及结合姿态预测等任务。
Trigger Scenarios
将配体对接到蛋白质
预测分子结合方式
为对接准备或清理PDB文件
寻找结合口袋或活性位点
对化合物库进行虚拟筛选
对接姿态打分及相互作用分析
对接结果排名以寻找最佳结合物
Install
npx skills add synthetic-sciences/openscience --skill molecular-docking -g -y
SKILL.md
Frontmatter
{
"name": "molecular-docking",
"license": "MIT",
"category": "chemistry",
"metadata": {
"skill-author": "Synthetic Sciences"
},
"description": "End-to-end molecular docking pipeline. Target preparation, pocket detection, protein-ligand docking (DiffDock\/Vina), scoring, interaction analysis, and pose ranking."
}
Molecular Docking Pipeline
Overview
This skill provides a complete end-to-end molecular docking workflow covering every stage from raw protein structure to ranked, annotated binding poses. It integrates classical physics-based docking (AutoDock Vina) with modern deep-learning approaches (DiffDock), and includes protein-ligand interaction fingerprinting for downstream analysis.
Pipeline Stages:
- Target Preparation -- Clean PDB structures, remove waters, add hydrogens, detect binding pockets
- Ligand Preparation -- Convert SMILES to 3D, generate conformers, assign charges
- Docking -- Run Vina or DiffDock to generate binding poses
- Scoring & Interaction Analysis -- Identify hydrogen bonds, hydrophobic contacts, pi-stacking, salt bridges
- Ranking -- Combine docking scores with interaction quality into a composite ranking
When to Use This Skill
Use this skill when the user requests any of the following:
- "Dock this ligand to a protein" or "predict how a molecule binds"
- "Prepare a protein for docking" or "clean this PDB file"
- "Find binding pockets" or "detect active sites"
- "Run virtual screening against a compound library"
- "Score docked poses" or "analyze protein-ligand interactions"
- "Rank docking results" or "find the best binders"
- Any structure-based drug design task involving PDB files and small molecules
- Lead optimization where binding pose context is needed
Do NOT use this skill for:
- Binding affinity prediction (use MM/GBSA or free energy perturbation tools)
- Protein-protein docking (use HDOCK or ClusPro)
- Covalent docking (requires specialized workflows)
- Homology modeling (use AlphaFold or ESMFold first, then dock)
Related Skills
- diffdock: For DiffDock-specific deep learning docking with all configuration options. This pipeline skill already calls DiffDock internally.
- denovo-design: For generating novel molecules to dock. Combine with this skill for a complete design-dock workflow.
- admet-prediction: For filtering docking hits by ADMET properties before experimental testing.
Installation
Python version: Python 3.11 required. rdkit-pypi has no wheels for Python 3.12+. Create your venv with uv venv --python 3.11 or python3.11 -m venv .venv.
Required Dependencies
# Core (required for all stages) — pin numpy<2 for rdkit-pypi compatibility
pip install rdkit-pypi biopython "numpy<2" scipy
# PDBQT conversion — OpenBabel provides reliable Gasteiger charge computation.
# Recommended: install openbabel-wheel for best docking accuracy.
pip install openbabel-wheel
# Target preparation
pip install biopython
# Ligand preparation
pip install rdkit-pypi
# Docking -- Vina pathway
# Note: meeko 0.7.x requires rdkit >= 2023.x. If using rdkit-pypi 2022.9.5,
# install meeko 0.5.x instead: pip install "meeko<0.6"
pip install meeko vina
# Docking -- DiffDock pathway (optional, GPU recommended)
# See https://github.com/gcorso/DiffDock for installation
# Interaction analysis
pip install prolif
# Recommended extras
pip install pandas
Quick Verification
python -c "from rdkit import Chem; print('RDKit OK')"
python -c "from Bio.PDB import PDBParser; print('BioPython OK')"
python -c "from vina import Vina; print('Vina OK')"
python -c "import meeko; print('Meeko OK')"
python -c "import prolif; print('ProLIF OK')"
python -c "import shutil; print('OpenBabel:', 'OK' if shutil.which('obabel') else 'not found (fallback charges used)')"
Core Workflows
Workflow 1: Single Ligand Docking
Dock one ligand to one protein target from start to finish.
# Step 1: Prepare target
python scripts/prepare_target.py \
--input protein.pdb \
--output prepared_protein.pdb \
--detect-pockets
# Step 2: Prepare ligand
python scripts/prepare_ligands.py \
--input "CCO" \
--output ligand.sdf
# Step 3: Dock
python scripts/dock.py \
--protein prepared_protein.pdb \
--ligand ligand.sdf \
--output-dir docking_results/ \
--method vina \
--center_x 10.0 --center_y 20.0 --center_z 15.0
# Step 4: Score and analyze interactions
python scripts/score.py \
--protein prepared_protein.pdb \
--poses docking_results/poses.sdf \
--output interactions.json
# Step 5: Rank
python scripts/rank.py \
--scores docking_results/scores.csv \
--interactions interactions.json \
--output ranked_results.csv \
--top-n 5
Workflow 2: Virtual Screening
Screen a library of compounds against a single target.
# Prepare target once
python scripts/prepare_target.py \
--input target.pdb \
--output prepared_target.pdb \
--detect-pockets
# Prepare compound library (CSV with name,smiles columns)
python scripts/prepare_ligands.py \
--input compounds.csv \
--output library.sdf
# Dock entire library
python scripts/dock.py \
--protein prepared_target.pdb \
--ligand library.sdf \
--output-dir vs_results/ \
--method vina \
--exhaustiveness 32 \
--num-poses 5
# Score all results
python scripts/score.py \
--protein prepared_target.pdb \
--poses vs_results/poses.sdf \
--output vs_interactions.json
# Rank and get top hits
python scripts/rank.py \
--scores vs_results/scores.csv \
--interactions vs_interactions.json \
--output vs_ranked.csv \
--top-n 20
Workflow 3: Rescoring Existing Poses
Rescore and re-rank poses from a previous docking run or from an external tool.
# Score existing poses
python scripts/score.py \
--protein protein.pdb \
--poses existing_poses.sdf \
--output rescored.json
# Rank with interaction data
python scripts/rank.py \
--scores original_scores.csv \
--interactions rescored.json \
--output reranked.csv
Workflow 4: Zero-Config Pipeline with Pocket Detection
Use --pockets or --auto-detect-pockets for automatic pocket-aware docking without manually specifying box coordinates.
# Option A: Use pre-computed pockets from pocket-detection skill
python ../pocket-detection/scripts/detect.py \
--input protein.pdb --output pockets.json
python scripts/dock.py \
--protein protein.pdb \
--ligand ligand.sdf \
--output-dir results/ \
--pockets pockets.json
# Option B: Auto-detect pockets on the fly
python scripts/dock.py \
--protein protein.pdb \
--ligand ligand.sdf \
--output-dir results/ \
--auto-detect-pockets
Pocket discovery priority: --pockets flag > protein_pockets.json > pockets.json > druggability.json > auto-detect > geometric center.
Script Reference
| Script | Purpose | Key Inputs | Key Outputs |
|---|---|---|---|
scripts/prepare_target.py |
Clean protein, detect pockets | PDB file | Prepared PDB + pocket JSON |
scripts/prepare_ligands.py |
SMILES/SDF to 3D conformers | SMILES, CSV, or SDF | Multi-molecule SDF |
scripts/dock.py |
Run docking (Vina/DiffDock) | Protein PDB + Ligand SDF | Poses SDF + scores CSV |
scripts/score.py |
Interaction fingerprinting | Protein PDB + Poses SDF | Interaction JSON/CSV |
scripts/rank.py |
Composite ranking | Scores CSV + Interactions JSON | Ranked summary CSV |
Output Interpretation
Docking Scores (Vina)
- Score (kcal/mol): More negative = stronger predicted binding. Typical drug-like: -6 to -12 kcal/mol.
- RMSD Lower Bound: Deviation from the best pose. Poses with RMSD < 2.0 A from reference are considered accurate.
- Scores below -7.0 kcal/mol are generally considered promising hits.
Interaction Analysis
- Hydrogen Bonds: Distance < 3.5 A between donor-acceptor, angle > 120 degrees. Key for specificity.
- Hydrophobic Contacts: Non-polar atoms within 4.5 A. Contribute to binding entropy.
- Pi-Stacking: Aromatic ring centroids within 5.5 A, angle < 30 degrees (parallel) or > 60 degrees (T-shaped).
- Salt Bridges: Charged groups within 4.0 A. Strong electrostatic contribution.
- Halogen Bonds: C-X...Y angle ~165 degrees, distance < 3.5 A.
Composite Ranking
The ranking script combines docking score (normalized) with interaction quality metrics. A compound ranking highly should have both a favorable docking score AND meaningful protein-ligand interactions -- this reduces false positives from scoring function artifacts.
Pocket Detection
See references/pocket_detection.md for detailed guidance on interpreting detected pockets, druggability assessment, and manual pocket specification strategies.
Troubleshooting
- Vina fails with "atom type not found": Ensure the protein PDB has no exotic elements. Run
prepare_target.pyfirst. - RDKit embedding fails: The SMILES may represent a molecule that is hard to embed in 3D. Try adding
--ph 7.0or check SMILES validity. - DiffDock not found: DiffDock requires a separate installation with PyTorch Geometric. Fall back to
--method vina. - No pockets detected: The protein may lack a clear cavity. Provide manual coordinates via
--center_x/y/zin the docking step. - ProLIF import error: Install with
pip install prolif. Requires RDKit and MDAnalysis.
Version History
- e9844a4 Current 2026-07-11 17:21
