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diffdock skill

by K-Dense-AI·K-Dense-AI/scientific-agent-skills·47k stars·MIT

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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Install the diffdock skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description. 

git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills.git /tmp/scientific-agent-skills
mkdir -p ~/.claude/skills
cp -r /tmp/scientific-agent-skills/skills/diffdock ~/.claude/skills/diffdock
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

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
  • Current upstream release is DiffDock v1.1.3; DiffDock-L is the default model line in defaultinferenceargs.yaml

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_description "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_description ligand.sdf \
  --out_dir results/sequence_docking/

Output Structure:

results/single_docking/
└── complex_0/
    ├── rank1.sdf                    # Convenience copy of top-ranked pose
    ├── rank1_confidence0.87.sdf     # Top-ranked pose with confidence in filename
    ├── rank2_confidence0.42.sdf     # Second-ranked pose
    ├── ...
    └── rank10_confidence-1.23.sdf   # 10th pose (default: 10 samples)

Current inference.py registers --liganddescription for single-complex runs. Some upstream README text still says --ligand; use --liganddescription 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:

# 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:

Critical Notes:

  1. Confidence ≠ Affinity: High confidence means model certainty about structure, NOT strong binding
  2. Context Matters: Adjust expectations for:
  • Large ligands (>500 Da): Lower confidence expected
  • Multiple protein chains: May decrease confidence
  • Novel protein families: May underperform
  1. Multiple Samples: Review top 3-5 predictions, look for consensus

For detailed guidance: Read references/confidenceandlimitations.md using the Read tool

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