| name | diffdock-molecular-docking |
| description | 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 | MIT |
| author | 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.
