DiffDock: protein-small-molecule docking

DiffDock-L generates candidate ligand poses and ranks them by model confidence. Confidence is neither a measured probability of correctness nor binding affinity. Use this skill for one pair, batches, or separate receptor conformations; treat library-wide confidence sorting as pose triage, not hit identification.

Verified scope

Targets the current v1.1.3 release. Released source and current main's identical inference.py were checked on 2026-09-30. Bundled helpers were tested on tiny synthetic inputs; pretrained docking, ESMFold, CUDA, Docker, GNINA, and hosted-demo execution were not run in this review. Commands requiring those components are source-verified recipes, not successful end-to-end demonstrations.

Set up the upstream environment

git clone --branch v1.1.3 --depth 1 https://github.com/gcorso/DiffDock.git
cd DiffDock
conda env create --file environment.yml
conda activate diffdock

The upstream environment pins Python 3.9.18, CUDA 11.7 Torch/PyG wheels and old scientific dependencies. Do not substitute current torch or the unrelated PyPI esm package for fair-esm. The published CUDA environment is not a macOS-native installation recipe. Follow upstream Docker instructions if suitable:

docker pull rbgcsail/diffdock
docker run -it --gpus all --entrypoint /bin/bash rbgcsail/diffdock
micromamba activate diffdock

Record the image digest: its unversioned tag need not equal the checked source. PDB-based inference has a CPU path; sequence folding calls .cuda() unconditionally. ESM2 embeddings are needed even for PDB inputs. First use can download docking, ESM2 and (for sequence inputs) ESMFold weights and build SO(2)/SO(3) tables. Budget storage and memory for all components; do not assume a single small checkpoint.

Run this skill's checker from the DiffDock checkout using its absolute path: