diffdock

name diffdock description DiffDock and DiffDock-L molecular docking. Use for protein-small-molecule pose prediction from PDB or sequence plus SMILES/SDF/MOL2, batch docking, virtual screening, and pose-confidence interpretation. Not for binding affinity prediction. allowed-tools Read Write Edit Bash Glob Grep compatibility Requires the DiffDock repository, Python 3.9 environment from upstream environment.yml or the official Docker image, RDKit, PyTorch/PyG, and optional CUDA GPU acceleration. Current guidance targets DiffDock v1.1.3 / DiffDock-L. license MIT license metadata {"version":"1.3","skill-author":"K-Dense Inc."} DiffDock: Molecular Docking with Diffusion Models Overview DiffDock is a diffusion-based deep learning tool for molecular docking that predicts 3D binding poses of small molecule ligands to protein targets. It represents the state-of-the-art in computational docking, crucial for structure-based drug discovery and chemical biology. Core Capabilities: Predict ligand binding poses with high accuracy using deep learning Support protein structures (PDB files) or sequences (via ESMFold) Process single complexes or batch virtual screening campaigns Generate confidence scores to assess prediction reliability Handle diverse ligand inputs (SMILES, SDF, MOL2) Key Distinction: DiffDock predicts binding poses (3D structure) and confidence (prediction certainty), NOT binding affinity (ΔG, Kd). Always combine with scoring functions (GNINA, MM/GBSA) for affinity assessment. When to Use This Skill This skill should be used when: "Dock this ligand to a protein" or "predict binding pose" "Run molecular docking" or "perform protein-ligand docking" "Virtual screening" or "screen compound library" "Where does this molecule bind?" or "predict binding site" Structure-based drug design or lead optimization tasks Tasks involving PDB files + SMILES strings or ligand structures Batch docking of multiple protein-ligand pairs Installation and Environment Setup Check Environment Status Before proceeding with DiffDock tasks, verify the environment setup: # Use the provided setup checker python scripts/setup_check.py This script validates Python version, PyTorch with CUDA, PyTorch Geometric, RDKit, ESM, and other dependencies. Installation Options Option 1: Conda (Recommended) git clone https://github.com/gcorso/DiffDock.git cd DiffDock conda env create --file environment.yml conda activate diffdock Option 2: Docker docker pull rbgcsail/diffdock docker run -it --gpus all --entrypoint /bin/bash rbgcsail/diffdock micromamba activate diffdock Important Notes: GPU strongly recommended (10-100x speedup vs CPU) First run pre-computes SO(2)/SO(3) lookup tables (~2-5 minutes) Model checkpoints (~500MB) download automatically if not present Current upstream release is DiffDock v1.1.3; DiffDock-L is the default model line in default_inference_args.yaml Core Workflows Workflow 1: Single Protein-Ligand Docking Use Case: Dock one ligand to one protein target Input Requirements: Protein: PDB file OR amino acid sequence Ligand: SMILES string OR structure file (SDF/MOL2) Command: python -m inference \ --config default_inference_args.yaml \ --protein_path protein.pdb \ --ligand_description "CC(=O)Oc1ccccc1C(=O)O" \ --out_dir results/single_docking/ Alternative (protein sequence): python -m inference \ --config default_inference_args.yaml \ --protein_sequence "MSKGEELFTGVVPILVELDGDVNGHKF..." \ --ligand_description ligand.sdf \ --out_dir results/sequence_docking/ Output Structure: results/single_docking/ └── complex_0/ ├── rank1.sdf # Convenience copy of top-ranked pose ├── rank1_confidence0.87.sdf # Top-ranked pose with confidence in filename ├── rank2_confidence0.42.sdf # Second-ranked pose ├── ... └── rank10_confidence-1.23.sdf # 10th pose (default: 10 samples) Current inference.py registers --ligand_description for single-complex runs. Some upstream README text still says --ligand ; use --ligand_description unless your local checkout explicitly supports a --ligand alias. Workflow 2: Batch Processing Multiple Complexes Use Case: Dock multiple ligands to proteins, virtual screening campaigns Step 1: Prepare Batch CSV Use the provided script to create or validate batch input: # Create template python scripts/prepare_batch_csv.py --create --output batch_input.csv # Validate existing CSV python scripts/prepare_batch_csv.py my_input.csv --validate CSV Format: complex_name,protein_path,ligand_description,protein_sequence complex1,protein1.pdb,CC(=O)Oc1ccccc1C(=O)O, complex2,,COc1ccc(C#N)cc1,MSKGEELFT... complex3,protein3.pdb,ligand3.sdf, Required Columns: complex_name : Unique identifier protein_path : PDB file path (leave empty if using sequence) ligand_description : SMILES string or ligand file path protein_sequence : Amino acid sequence (leave empty if using PDB) Step 2: Run Batch Docking python -m inference \ --config default_inference_args.yaml \ --protein_ligand_csv batch_input.csv \ --out_dir results/batch/ \ --batch_size 10 For Large Virtual Screening (>100 compounds): Pre-compute protein embeddings for faster processing: # Pre-compute embeddings python datasets/esm_embedding_preparation.py \ --protein_ligand_csv screening_input.csv \ --out_file protein_embeddings.pt # Run with pre-computed embeddings python -m inference \ --config default_inference_args.yaml \ --protein_ligand_csv screening_input.csv \ --esm_embeddings_path protein_embeddings.pt \ --out_dir results/screening/ Workflow 3: Analyzing Results After docking completes, analyze confidence scores and rank predictions: # Analyze all results python scripts/analyze_results.py results/batch/ # Show top 5 per complex python scripts/analyze_results.py results/batch/ --top 5 # Filter by confidence threshold python scripts/analyze_results.py results/batch/ --threshold 0.0 # Export to CSV python scripts/analyze_results.py results/batch/ -- export summary.csv # Show top 20 predictions across all complexes python scripts/analyze_results.py results/batch/ --best 20 The analysis script: Parses confidence scores from all predictions Classifies as High (>0), Moderate (-1.5 to 0), or Low (<-1.5) Ranks predictions within and across complexes Generates statistical summaries Exports results to CSV for downstream analysis Confidence Score Interpretation Understanding Scores: Score Range Confidence Level Interpretation > 0 High Strong prediction, likely accurate -1.5 to 0 Moderate Reasonable prediction, validate carefully < -1.5 Low Uncertain prediction, requires validation Critical Notes: Confidence ≠ Affinity : High confidence means model certainty about structure, NOT strong binding Context Matters : Adjust expectations for: Large ligands (>500 Da): Lower confidence expected Multiple protein chains: May decrease confidence Novel protein families: May underperform Multiple Samples : Review top 3-5 predictions, look for consensus For detailed guidance: Read references/confidence_and_limitations.md using the Read tool Parameter Customization Using Custom Configuration Create custom configuration for specific use cases: # Copy template cp assets/custom_inference_config.yaml my_config.yaml # Edit parameters (see template for presets) # Then run with custom config python -m inference \ --config my_config.yaml \ --protein_ligand_csv input.csv \ --out_dir results/ Key Parameters to Adjust Sampling Density: samples_per_complex: 10 → Increase to 20-40 for difficult cases More samples = better coverage but longer runtime Inference Steps: inference_steps: 20 → Increase to 25-30 for higher accuracy More steps = potentially better quality but slower Temperature Parameters (control diversity): temp_sampling_tor: 7.04 → Increase for flexible ligands (8-10) temp_sampling_tor: 7.04 → Decrease for rigid ligands (5-6) Higher temperature = more diverse poses Presets Available in Template: High Accuracy: More samples + steps, lower temperature Fast Screening: Fewer samples, faster Flexible Ligands: Increased torsion temperature Rigid Ligands: Decreased torsion temperature For complete parameter reference: Read references/parameters_reference.md using the Read tool Advanced Techniques Ensemble Docking (Protein Flexibility) For proteins with known flexibility, dock to multiple conformations: # Create ensemble CSV import pandas as pd conformations = [ "conf1.pdb" , "conf2.pdb" , "conf3.pdb" ] ligand = "CC(=O)Oc1ccccc1C(=O)O" data = { "complex_name" : [ f"ensemble_ {i} " for i in range ( len (conformations))], "protein_path" : conformations, "ligand_description" : [ligand] * len (conformations), "protein_sequence" : [ "" ] * len (conformations) } pd.DataFrame(data).to_csv( "ensemble_input.csv" , index= False ) Run docking with increased sampling: python -m inference \ --config default_inference_args.yaml \ --protein_ligand_csv ensemble_input.csv \ --samples_per_complex 20 \ --out_dir results/ensemble/ Integration with Scoring Functions DiffDock generates poses; combine with other tools for affinity: GNINA (Fast neural network scoring): for pose in results/single_docking/complex_0/*confidence*.sdf; do gnina -r protein.pdb -l " $pose " --score_only done MM/GBSA (More accurate, slower): Use AmberTools MMPBSA.py or gmx_MMPBSA after energy minimization Free Energy Calculations (Most accurate): Use OpenMM + OpenFE or GROMACS for FEP/TI calculations Recommended Workflow: DiffDock → Generate poses with confidence scores Visual inspection → Check structural plausibility GNINA or MM/GBSA → Rescore and rank by affinity Experimental validation → Biochemical assays Limitations and Scope DiffDock IS Designed For: Small molecule ligands (typically 100-1000 Da) Drug-like organic compounds Small peptides (<20 residues) Single or multi-chain proteins DiffDock IS NOT Designed For: Large biomolecules (protein-protein docking) → Use DiffDock-PP or AlphaFold-Multimer Large peptides (>20 residues) → Use alternative methods Covalent docking → Use specialized covalent docking tools Binding affinity prediction → Combine with scoring functions Membrane proteins → Not specifically trained, use with caution For complete limitations: Read references/confidence_and_limitations.md using the Read tool Troubleshooting Common Issues Issue: Low confidence scores across all predictions Cause: Large/unusual ligands, unclear binding site, protein flexibility Solution: Increase samples_per_complex (20-40), try ensemble docking, validate protein structure Issue: Out of memory errors Cause: GPU memory insufficient for batch size Solution: Reduce --batch_size 2 or process fewer complexes at once Issue: Slow performance Cause: Running on CPU instead of GPU Solution: Verify CUDA with python -c "import torch; print(torch.cuda.is_available())" , use GPU Issue: Unrealistic binding poses Cause: Poor protein preparation, ligand too large, wrong binding site Solution: Check protein for missing residues, remove far waters, consider specifying binding site Issue: "Module not found" errors Cause: Missing dependencies or wrong environment Solution: Run python scripts/setup_check.py to diagnose Performance Optimization For Best Results: Use GPU (essential for practical use) Pre-compute ESM embeddings for repeated protein use Batch process multiple complexes together Start with default parameters, then tune if needed Validate protein structures (resolve missing residues) Use canonical SMILES for ligands Graphical User Interface For interactive use, launch the web interface: python app/main.py # Navigate to http://localhost:7860 Or use the online demo without installation: https://huggingface.co/spaces/reginabarzilaygroup/DiffDock-Web Resources Helper Scripts ( scripts/ ) prepare_batch_csv.py : Create and validate batch input CSV files Create templates with example entries Validate file paths and SMILES strings Check for required columns and format issues analyze_results.py : Analyze confidence scores and rank predictions Parse results from single or batch runs Generate statistical summaries Export to CSV for downstream analysis Identify top predictions across complexes setup_check.py : Verify DiffDock environment setup Check Python version and dependencies Verify PyTorch and CUDA availability Test RDKit and PyTorch Geometric installation Provide installation instructions if needed Reference Documentation ( references/ ) parameters_reference.md : Complete parameter documentation All command-line options and configuration parameters Default values and acceptable ranges Temperature parameters for controlling diversity Model checkpoint locations and version flags Read this file when users need: Detailed parameter explanations Fine-tuning guidance for specific systems Alternative sampling strategies confidence_and_limitations.md : Confidence score interpretation and tool limitations Detailed confidence score interpretation When to trust predictions Scope and limitations of DiffDock Integration with complementary tools Troubleshooting prediction quality Read this file when users need: Help interpreting confidence scores Understanding when NOT to use DiffDock Guidance on combining with other tools Validation strategies workflows_examples.md : Comprehensive workflow examples Detailed installation instructions Step-by-step examples for all workflows Advanced integration patterns Troubleshooting common issues Best practices and optimization tips Read this file when users need: Complete workflow examples with code Integration with GNINA, OpenMM, or other tools Virtual screening workflows Ensemble docking procedures Assets ( assets/ ) batch_template.csv : Template for batch processing Pre-formatted CSV with required columns Example entries showing different input types Ready to customize with actual data custom_inference_config.yaml : Configuration template Annotated YAML with all parameters Four preset configurations for common use cases Detailed comments explaining each parameter Ready to customize and use Best Practices Always verify environment with setup_check.py before starting large jobs Validate batch CSVs with prepare_batch_csv.py to catch errors early Start with defaults then tune parameters based on system-specific needs Generate multiple samples (10-40) for robust predictions Visual inspection of top poses before downstream analysis Combine with scoring functions for affinity assessment Use confidence scores for initial ranking, not final decisions Pre-compute embeddings for virtual screening campaigns Document parameters used for reproducibility Validate results experimentally when possible Citations When using DiffDock, cite the appropriate papers: DiffDock-L (current default model): Corso et al. (2024) "Deep Confident Steps to New Pockets: Strategies for Docking Generalization", ICLR 2024, arXiv:2402.18396