molecular-docking

name molecular-docking description End-to-end molecular docking pipeline. Target preparation, pocket detection, protein-ligand docking (DiffDock/Vina), scoring, interaction analysis, and pose ranking. category chemistry license MIT metadata {"skill-author":"Synthetic Sciences"} 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.py first. RDKit embedding fails : The SMILES may represent a molecule that is hard to embed in 3D. Try adding --ph 7.0 or 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/z in the docking step. ProLIF import error : Install with pip install prolif . Requires RDKit and MDAnalysis.