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.
Best use case
diffdock-molecular-docking is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using diffdock-molecular-docking should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/diffdock-molecular-docking/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How diffdock-molecular-docking Compares
| Feature / Agent | diffdock-molecular-docking | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
> **Source**: [https://github.com/aipoch/medical-research-skills](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 ```bash 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: ```bash 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`.
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