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references/confidence_and_limitations.md

DiffDock Confidence Scores and Limitations

This document provides detailed guidance on interpreting DiffDock confidence scores and understanding the tool's limitations.

Confidence Score Interpretation

DiffDock generates a confidence score for each predicted binding pose. This score indicates the model's certainty about the prediction.

Score Ranges

Score Range Confidence Level Interpretation
> 0 High confidence Strong prediction, likely accurate binding pose
-1.5 to 0 Moderate confidence Reasonable prediction, may need validation
< -1.5 Low confidence Uncertain prediction, requires careful validation

Important Notes on Confidence Scores

  1. Not Binding Affinity: Confidence scores reflect prediction certainty, NOT binding affinity strength
  2. High confidence = model is confident about the structure

  3. Does NOT indicate strong/weak binding affinity

  4. Context-Dependent: Confidence scores should be adjusted based on system complexity:

  5. Lower expectations for:

    • Large ligands (>500 Da)
    • Protein complexes with many chains
    • Unbound protein conformations (may require conformational changes)
    • Novel protein families not well-represented in training data

  6. Higher expectations for:

    • Drug-like small molecules (150-500 Da)
    • Single-chain proteins or well-defined binding sites
    • Proteins similar to those in training data (PDBBind, BindingMOAD)

  7. Multiple Predictions: DiffDock generates multiple samples per complex (default: 10)

  8. Review top-ranked predictions (by confidence)
  9. Consider clustering similar poses
  10. High-confidence consensus across multiple samples strengthens prediction

What DiffDock Predicts

✅ DiffDock DOES Predict

  • Binding poses: 3D spatial orientation of ligand in protein binding site
  • Confidence scores: Model's certainty about predictions
  • Multiple conformations: Various possible binding modes

❌ DiffDock DOES NOT Predict

  • Binding affinity: Strength of protein-ligand interaction (ΔG, Kd, Ki)
  • Binding kinetics: On/off rates, residence time
  • ADMET properties: Absorption, distribution, metabolism, excretion, toxicity
  • Selectivity: Relative binding to different targets

Scope and Limitations

Designed For

  • Small molecule docking: Organic compounds typically 100-1000 Da
  • Protein targets: Single or multi-chain proteins
  • Small peptides: Short peptide ligands (< ~20 residues)
  • Small nucleic acids: Short oligonucleotides

NOT Designed For

  • Large biomolecules: Full protein-protein interactions
  • Use DiffDock-PP, AlphaFold-Multimer, or RoseTTAFold2NA instead
  • Large peptides/proteins: >20 residues as ligands
  • Covalent docking: Irreversible covalent bond formation
  • Metalloprotein specifics: May not accurately handle metal coordination
  • Membrane proteins: Not specifically trained on membrane-embedded proteins

Training Data Considerations

DiffDock was trained on: - PDBBind: Diverse protein-ligand complexes - BindingMOAD: Multi-domain protein structures

Implications: - Best performance on proteins/ligands similar to training data - May underperform on: - Novel protein families - Unusual ligand chemotypes - Allosteric sites not well-represented in training data

Validation and Complementary Tools

  1. Generate poses with DiffDock
  2. Use confidence scores for initial ranking

  3. Consider multiple high-confidence predictions

  4. Visual Inspection

  5. Examine protein-ligand interactions in molecular viewer

  6. Check for reasonable:

    • Hydrogen bonds
    • Hydrophobic interactions
    • Steric complementarity
    • Electrostatic interactions

  7. Scoring and Refinement (choose one or more):

  8. GNINA: Deep learning-based scoring function
  9. Molecular mechanics: Energy minimization and refinement
  10. MM/GBSA or MM/PBSA: Binding free energy estimation

  11. Free energy calculations: FEP or TI for accurate affinity prediction

  12. Experimental Validation

  13. Biochemical assays (IC50, Kd measurements)
  14. Structural validation (X-ray crystallography, cryo-EM)

Tools for Binding Affinity Assessment

DiffDock should be combined with these tools for affinity prediction:

  • GNINA: Fast, accurate scoring function

  • Github: github.com/gnina/gnina

  • AutoDock Vina: Classical docking and scoring

  • Website: vina.scripps.edu

  • Free Energy Calculations:

  • OpenMM + OpenFE

  • GROMACS + ABFE/RBFE protocols

  • MM/GBSA Tools:

  • MMPBSA.py (AmberTools)
  • gmx_MMPBSA

Performance Optimization

For Best Results

  1. Protein Preparation:
  2. Remove water molecules far from binding site
  3. Resolve missing residues if possible

  4. Consider protonation states at physiological pH

  5. Ligand Input:

  6. Provide reasonable 3D conformers when using structure files
  7. Use canonical SMILES for consistent results

  8. Pre-process with RDKit if needed

  9. Computational Resources:

  10. GPU strongly recommended (10-100x speedup)
  11. First run pre-computes lookup tables (takes a few minutes)

  12. Batch processing more efficient than single predictions

  13. Parameter Tuning:

  14. Increase samples_per_complex for difficult cases (20-40)
  15. Adjust temperature parameters for diversity/accuracy trade-off
  16. Use pre-computed ESM embeddings for repeated predictions

Common Issues and Troubleshooting

Low Confidence Scores

  • Large/flexible ligands: Consider splitting into fragments or use alternative methods
  • Multiple binding sites: May predict multiple locations with distributed confidence
  • Protein flexibility: Consider using ensemble of protein conformations

Unrealistic Predictions

  • Clashes: May indicate need for protein preparation or refinement
  • Surface binding: Check if true binding site is blocked or unclear
  • Unusual poses: Consider increasing samples to explore more conformations

Slow Performance

  • Use GPU: Essential for reasonable runtime
  • Pre-compute embeddings: Reuse ESM embeddings for same protein
  • Batch processing: More efficient than sequential individual predictions
  • Reduce samples: Lower samples_per_complex for quick screening

Citation and Further Reading

For methodology details and benchmarking results, see:

  1. Original DiffDock Paper (ICLR 2023):
  2. "DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking"

  3. Corso et al., arXiv:2210.01776

  4. DiffDock-L Paper (2024):

  5. Enhanced model with improved generalization

  6. Stärk et al., arXiv:2402.18396

  7. PoseBusters Benchmark:

  8. Rigorous docking evaluation framework
  9. Used for DiffDock validation
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