gh-k-dense-ai-claude-scientific-skills-scientific-skills/skills/diffdock/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

   • High confidence = model is confident about the structure
   • Does NOT indicate strong/weak binding affinity

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

   • 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

   • 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)

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

   • Review top-ranked predictions (by confidence)
   • Consider clustering similar poses
   • 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

Recommended Workflow

1. Generate poses with DiffDock

   • Use confidence scores for initial ranking
   • Consider multiple high-confidence predictions

2. Visual Inspection

   • Examine protein-ligand interactions in molecular viewer
   • Check for reasonable:
     • Hydrogen bonds
     • Hydrophobic interactions
     • Steric complementarity
     • Electrostatic interactions

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

   • GNINA: Deep learning-based scoring function
   • Molecular mechanics: Energy minimization and refinement
   • MM/GBSA or MM/PBSA: Binding free energy estimation
   • Free energy calculations: FEP or TI for accurate affinity prediction

4. Experimental Validation

   • Biochemical assays (IC50, Kd measurements)
   • 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:

   • Remove water molecules far from binding site
   • Resolve missing residues if possible
   • Consider protonation states at physiological pH

2. Ligand Input:

   • Provide reasonable 3D conformers when using structure files
   • Use canonical SMILES for consistent results
   • Pre-process with RDKit if needed

3. Computational Resources:

   • GPU strongly recommended (10-100x speedup)
   • First run pre-computes lookup tables (takes a few minutes)
   • Batch processing more efficient than single predictions

4. Parameter Tuning:

   • Increase samples_per_complex for difficult cases (20-40)
   • Adjust temperature parameters for diversity/accuracy trade-off
   • 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):

   • "DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking"
   • Corso et al., arXiv:2210.01776

2. DiffDock-L Paper (2024):

   • Enhanced model with improved generalization
   • Stärk et al., arXiv:2402.18396

3. PoseBusters Benchmark:

   • Rigorous docking evaluation framework
   • Used for DiffDock validation