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.

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: This script validates Python version, PyTorch with CUDA, PyTorch Geometric, RDKit, ESM, and other dependencies. Installation Options Option 1: Conda (Recommended) Option 2: Docker 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: Alternative (protein sequence): Output Structure: 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: CSV Format: 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 For Large Virtual Screening ( 100 compounds): Pre compute protein embeddings for faster processing: Workflow 3: Analyzing Results After docking completes, analyze confidence scores and rank predictions: 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: 1. Confidence ≠ Affinity : High confidence means model certainty about structure, NOT strong binding 2. Context Matters : Adjust expectations for: Large ligands ( 500 Da): Lower confidence expected Multiple protein chains: May decrease confidence Novel protein families: May underperform 3. 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: 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: 1. High Accuracy: More samples + steps, lower temperature 2. Fast Screening: Fewer samples, faster 3. Flexible Ligands: Increased torsion temperature 4. 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: Run docking with increased sampling: Integration with Scoring Functions DiffDock generates poses; combine with other tools for affinity: GNINA (Fast neural network scoring): 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: 1. DiffDock → Generate poses with confidence scores 2. Visual inspection → Check structural plausibility 3. GNINA or MM/GBSA → Rescore and rank by affinity 4. 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: 1. Use GPU (essential for practical use) 2. Pre compute ESM embeddings for repeated protein use 3. Batch process multiple complexes together 4. Start with default parameters, then tune if needed 5. Validate protein structures (resolve missing residues) 6. Use canonical SMILES for ligands Graphical User Interface For interactive use, launch the web interface: 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 1. Always verify environment with setup check.py before starting large jobs 2. Validate batch CSVs with prepare batch csv.py to catch errors early 3. Start with defaults then tune parameters based on system specific needs 4. Generate multiple samples (10 40) for robust predictions 5. Visual inspection of top poses before downstream analysis 6. Combine with scoring functions for affinity assessment 7. Use confidence scores for initial ranking, not final decisions 8. Pre compute embeddings for virtual screening campaigns 9. Document parameters used for reproducibility 10. 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 Original DiffDock: Corso et al. (2023) "DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking", ICLR 2023, arXiv:2210.01776 Additional Resources GitHub Repository : https://github.com/gcorso/DiffDock Online Demo : https://huggingface.co/spaces/reginabarzilaygroup/DiffDock Web DiffDock L Paper : https://arxiv.org/abs/2402.18396 Original Paper : https://arxiv.org/abs/2210.01776 Citing Scientific Agent Skills This skill is part of Scientific Agent Skills by K Dense

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