DiffDock

Use this repo skill when a user is working with DiffDock: diffusion-based small-molecule docking to protein structures, DiffDock-L inference, the Gradio UI, training/data preparation, or benchmark evaluation.

Start Here

1. Read install-and-runtime.md for setup routes, dependency families, backend expectations, and safe preflight checks.
2. Use scripts/check_runtime_environment.py to inspect a candidate runtime without launching docking, training, or benchmarks.
3. Route to the focused sub-skill below, then use its bundled references and helper scripts before running expensive commands.
4. Read troubleshooting.md when failures involve imports, CUDA/Torch/PyG, RDKit/ProDy/ESM/OpenFold, model checkpoints, network downloads, data paths, or GNINA.
5. Check repo-provenance.md before deciding whether this skill is current for a checkout.

Routes

• Docking prediction: Use docking-inference for single-complex or batch CSV inference, input validation, command construction, model/config overrides, output files, and confidence-ranked poses.
• Web interface: Use web-ui for the Gradio app, single-complex UI inputs, launch/deploy issues, and downloaded output zip inspection.
• Training and data: Use training-data for score/confidence model training plans, dataset layouts, split files, ESM embeddings, cache checks, and checkpoint compatibility.
• Benchmarks and metrics: Use evaluation-benchmarks for PDBBind, BindingMOAD/DockGen, PoseBusters evaluation, RMSD/confidence metrics, GNINA post-processing, and vendored spyrmsd checks.

Repository Model

DiffDock is a script-style research repository rather than a package with console entry points. Future agents should use the bundled command builders to assemble commands, then run those commands only in a user-provided DiffDock runtime checkout or project context that contains the expected DiffDock modules, data, checkpoints, and dependencies.

The generated skill is self-contained for planning, validation, troubleshooting, and command construction. It does not bundle model weights, processed benchmark datasets, GNINA, ESM models, or the full DiffDock source tree.

Safe Defaults

• Prefer PDB protein inputs over sequence folding for lightweight inference smoke tests.
• Use command builders and validators first; they do not import the heavy DiffDock runtime stack.
• Treat full inference, Gradio launch, ESM extraction, training, benchmark evaluation, GNINA, and model downloads as potentially long-running or network/backend dependent.
• Keep user data paths explicit; do not assume the original example or dataset paths exist.
• Preserve model_parameters.yml beside checkpoints when moving trained score or confidence models.

Required References

• install-and-runtime.md covers setup, Docker/conda expectations, optional dependencies, and environment checks.
• troubleshooting.md covers cross-cutting failure modes shared by sub-skills.
• repo-routing-metadata.json is consumed by DisCo's managed repo-skills-router during import.
• repo-provenance.md records the source snapshot and evidence baseline.