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diffdock Diffusion-based molecular docking. Predict protein-ligand binding poses from PDB/SMILES, confidence scores, virtual screening, for structure-based drug design. Not for affinity prediction.
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9 arquivos name diffdock description Diffusion-based molecular docking. Predict protein-ligand binding poses from PDB/SMILES, confidence scores, virtual screening, for structure-based drug design. Not for affinity prediction. license MIT license tags ["protein-ligand-docking","binding-pose-prediction","diffusion-docking","virtual-screening","diffdock"] metadata {"skill-author":"K-Dense Inc."}
------|------------------|----------------|
| > 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 bindingContext 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
Parameter Customization
Using Custom Configuration Create custom configuration for specific use cases:
cp assets/custom_inference_config.yaml my_config.yaml
python -m inference \
--config my_config.yaml \
--protein_ligand_csv input.csv \
--out_dir results/
Key Parameters to Adjust
samples_per_complex: 10 → Increase to 20-40 for difficult casesMore samples = better coverage but longer runtime
inference_steps: 20 → Increase to 25-30 for higher accuracyMore 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:
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/*.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
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
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
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:
Or use the online demo without installation:
Resources
Helper Scripts (scripts/) prepare_batch_csv.py
Create templates with example entries
Validate file paths and SMILES strings
Check for required columns and format issues
analyze_results.py
Parse results from single or batch runs
Generate statistical summaries
Export to CSV for downstream analysis
Identify top predictions across complexes
setup_check.py
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
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
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
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
Pre-formatted CSV with required columns
Example entries showing different input types
Ready to customize with actual data
custom_inference_config.yaml
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 jobsValidate batch CSVs with prepare_batch_csv.py to catch errors earlyStart with defaults then tune parameters based on system-specific needsGenerate multiple samples (10-40) for robust predictionsVisual inspection of top poses before downstream analysisCombine with scoring functions for affinity assessmentUse confidence scores for initial ranking, not final decisionsPre-compute embeddings for virtual screening campaignsDocument parameters used for reproducibilityValidate results experimentally when possible
Citations When using DiffDock, cite the appropriate papers:
DiffDock-L (current default model):
Stärk et al. (2024) "DiffDock-L: Improving Molecular Docking with Diffusion Models"
arXiv:2402.18396
Corso et al. (2023) "DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking"
ICLR 2023, arXiv:2210.01776
Additional Resources 