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diffdock-molecular-docking

Diffusion-based molecular docking to predict 3D ligand–protein binding poses (blind docking) with confidence scoring; use when you need pose prediction for drug discovery or virtual screening.

Published by aipoch ↗aipoch/medical-research-skills

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diffdock-molecular-docking

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Use diffdock-molecular-docking to predict and rank binding poses for [protein PDB structure] and [ligand SMILES], returning ranked SDF poses and confidence scores.

Required inputs

  • [protein PDB structure]
  • [ligand SMILES]

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SKILL.md

name: diffdock-molecular-dockingdescription: Diffusion-based molecular docking to predict 3D ligand–protein binding poses (blind docking) with confidence scoring; use when you need pose prediction for drug discovery or virtual screening.license: MITauthor: AIPOCH

Source: https://github.com/aipoch/medical-research-skills

DiffDock Molecular Docking

When to Use

  • Blind docking when you have a protein structure (PDB) and a ligand (SMILES) but no known binding site.
  • Pose prediction to generate multiple plausible 3D binding conformations and rank them.
  • Virtual screening support to quickly evaluate candidate ligands by predicted binding poses and confidence.
  • Drug discovery workflows where you need automated docking outputs (SDF poses + scores) for downstream analysis.
  • Batch/advanced docking when running many ligand–protein pairs or using alternative inputs (e.g., sequence-based workflows; see references/workflows_examples.md).

Key Features

  • Diffusion generative sampling to produce diverse ligand binding poses.
  • Confidence model scoring to rank predicted poses.
  • Simple CLI inference for single protein–ligand docking.
  • Batch/advanced workflows documented in references/workflows_examples.md.
  • Structured outputs including ranked SDF pose files and a confidence score report.

Dependencies

  • Python (version not specified)
  • PyTorch (version not specified)
  • PyTorch Geometric / PyG (version not specified)
  • RDKit (version not specified)
  • ESM (version not specified)

Example Usage

1) Verify the Environment

python scripts/setup_check.py

2) Run Standard Inference (Single Docking)

Dock a single ligand (SMILES) to a protein structure (PDB) and write results to an output directory:

python scripts/inference_runner.py \
  --protein ./data/protein.pdb \
  --ligand "CC(=O)Oc1ccccc1C(=O)O" \
  --out_dir ./results

Arguments

  • --protein: Path to the protein PDB file.
  • --ligand: Ligand SMILES string.
  • --out_dir: Output directory (default: results/).

3) Outputs

After inference, the tool produces:

  • Ranked SDF pose files (e.g., rank1.sdf, rank2.sdf, ...), each containing a predicted 3D binding pose.
  • Confidence score report: confidence_scores.txt, listing the score for each ranked pose.

Implementation Details

  • Pose generation: Uses a diffusion-based generative model to sample multiple candidate ligand poses relative to the protein target.
  • Ranking: A separate confidence model assigns a score to each sampled pose; poses are sorted by this score and saved as rank*.sdf.
  • Parameterization:
    • For the complete CLI argument list and defaults, see references/parameters_reference.md.
    • For confidence interpretation, known limitations, and expected accuracy/scope, see references/confidence_and_limitations.md.
  • Advanced workflows: Batch processing and alternative input configurations are documented in references/workflows_examples.md.