diffdock

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.

Installation Options

Option 1: Conda (Recommended)

git clone https://github.com/gcorso/DiffDock.git
cd DiffDock
conda env create --file environment.yml
conda activate diffdock

Option 2: Docker

docker pull rbgcsail/diffdock
docker run -it --gpus all --entrypoint /bin/bash rbgcsail/diffdock
micromamba activate diffdock

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

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:

python -m inference \
  --config default_inference_args.yaml \
  --protein_path protein.pdb \
  --ligand "CC(=O)Oc1ccccc1C(=O)O" \
  --out_dir results/single_docking/

Alternative (protein sequence):

python -m inference \
  --config default_inference_args.yaml \
  --protein_sequence "MSKGEELFTGVVPILVELDGDVNGHKF..." \
  --ligand ligand.sdf \
  --out_dir results/sequence_docking/

Output Structure:

results/single_docking/
├── rank_1.sdf          # Top-ranked pose
├── rank_2.sdf          # Second-ranked pose
├── ...
├── rank_10.sdf         # 10th pose (default: 10 samples)
└── confidence_scores.txt

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:

# Create template
python scripts/prepare_batch_csv.py --create --output batch_input.csv

# Validate existing CSV
python scripts/prepare_batch_csv.py my_input.csv --validate

CSV Format:

complex_name,protein_path,ligand_description,protein_sequence
complex1,protein1.pdb,CC(=O)Oc1ccccc1C(=O)O,
complex2,,COc1ccc(C#N)cc1,MSKGEELFT...
complex3,protein3.pdb,ligand3.sdf,

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

python -m inference \
  --config default_inference_args.yaml \
  --protein_ligand_csv batch_input.csv \
  --out_dir results/batch/ \
  --batch_size 10

For Large Virtual Screening (>100 compounds):

Pre-compute protein embeddings for faster processing:

# Pre-compute embeddings
python datasets/esm_embedding_preparation.py \
  --protein_ligand_csv screening_input.csv \
  --out_file protein_embeddings.pt

# Run with pre-computed embeddings
python -m inference \
  --config default_inference_args.yaml \
  --protein_ligand_csv screening_input.csv \
  --esm_embeddings_path protein_embeddings.pt \
  --out_dir results/screening/

Workflow 3: Analyzing Results

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:

1. Confidence ≠ Affinity: High confidence means model certainty about structure, NOT strong binding
2. Context Matters: Adjust expectations for:
3. Large ligands (>500 Da): Lower confidence expected
4. Multiple protein chains: May decrease confidence
5. Novel protein families: May underperform
6. 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 template
cp 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:

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:

# Create ensemble CSV
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(