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:

python /path/to/diffdock-skill/scripts/setup_check.py

It checks imports/files, not successful model loading, scientific validity, or full version compatibility. An existing but incomplete score-model directory suppresses upstream's automatic download; inspect checkpoint files when restoring a partial run.

Prepare traceable inputs

1. Select the biological assembly/chains and a defensible protonation/tautomer state. Record receptor and ligand identifiers, file hashes, preparation choices, source coordinates, software versions, and intended stereochemistry. Missing atoms, waters, cofactors, metal coordination, and induced fit need explicit judgment.
2. Use PDB for the receptor or a complete amino-acid sequence. No ellipses. Upstream ESM2 truncates each chain at 1022 residues; longer chains can cause graph/embedding mismatches. Do not silently trim a biological target to make a run pass.
3. Use a SMILES or ligand file. Source readers support .sdf, .mol2, .pdb, .pdbqt; SDF input uses its first record. Existing ligand coordinates are discarded and a conformer regenerated. Inputting a pose does not restrain docking.
4. Use a fresh output directory for each run. Reusing one can leave old rank files from failed or differently sampled jobs. Preserve the expected input-ID manifest.

Single pair

Run from the upstream repository root, with real prepared inputs:

python -m inference \
  --config default_inference_args.yaml \
  --protein_path protein.pdb \
  --ligand_description "CC(=O)Oc1ccccc1C(=O)O" \
  --out_dir results/run_001/ \
  --loglevel INFO

For sequence input, replace --protein_path with --protein_sequence and a full sequence; this adds ESMFold/CUDA requirements and structural uncertainty. Use the registered name --ligand_description, not argparse's implicit abbreviation --ligand from the README.

Typical output (scores shown here are illustrative):

results/run_001/complex_0/
  rank1.sdf
  rank1_confidence0.87.sdf
  rank2_confidence0.42.sdf
  ...
  rank10_confidence-1.23.sdf

rank1.sdf duplicates the top pose. Filename confidence is rounded to two decimals; upstream rank reflects the original model score. --save_visualisation additionally writes rank<N>_reverseprocess.pdb, not the SDFs themselves.

Batch and ensemble runs

CSV columns are complex_name,protein_path,ligand_description,protein_sequence. Use unique explicit names; paths resolve relative to the **inference working

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