gnina-docking

name	gnina-docking
description	Run reproducible local GNINA docking or CNN rescoring on protein-ligand systems. Use when GPU-assisted pose generation, refinement, or CNN reranking is wanted; keep CNN pose score, predicted affinity, and experimental affinity as distinct quantities.
license	MIT

GNINA Docking

Gate and preflight

Follow $cx-compute-environment; ask once for the pinned GNINA binary/container, model download, GPU compute, and any structure download. Require prepared inputs from $cx-molecular-input-preparation. Prefer the official release binary or a digest-pinned container over a source build.

Workflow

1. Record GNINA release, binary checksum/container digest, CUDA/GPU, CNN model, empirical scoring function, cnn_scoring mode, box/autobox source, seed, exhaustiveness, and number of modes.
2. Smoke-test the binary and one control complex. Use an experimental reference ligand for autoboxing only when it is legitimate for the evaluation split.
3. Run docking; retain SDF properties, raw log, receptor, reference ligand, and exact command under artifacts/<run-id>/gnina/.
4. Keep empirical energy, CNN pose score, and CNN affinity output in separate columns. Do not select whichever score makes the result look best.
5. Validate redocking and ranking with $cx-docking-validation.

Boundaries

• CNN confidence/score is not experimental affinity and is not calibrated across arbitrary targets or model versions.
• Blind whole-protein docking needs stronger controls and higher search effort; label it exploratory.
• Flexible-sidechain docking requires a justified residue set and separate validation; more flexibility does not guarantee realism.