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.
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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":"1.2","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.
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:
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 templatecp 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:
