diffdock

DiffDock-L

DiffDock-L is a blind pose predictor: given a protein structure and a ligand, it samples ligand placements over the whole surface with a diffusion model and ranks them with a separately trained confidence head. The confidence score correlates with pose correctness, not with binding free energy — DiffDock does not predict whether or how tightly the ligand binds, so for hit triage you still pair it with a scorer (GNINA, MM-GBSA) or with boltz's affinity head. For protein–protein and nucleic-acid co-folding, route to boltz or chai1. Code and weights are MIT (github.com/gcorso/DiffDock).

Running it

cd $DIFFDOCK_REPO   # a clone of github.com/gcorso/DiffDock
python3 -m inference \
  --config default_inference_args.yaml \
  --protein_path target.pdb \
  --ligand_description "COc1ccc(C#N)cc1" \
  --out_dir out

For more than one complex, give --protein_ligand_csv batch.csv instead of the two single-complex flags; the CSV has four columns — complex_name, protein_path, ligand_description (SMILES or an .sdf/.mol2 path), and protein_sequence. Leave protein_path empty and fill protein_sequence to have DiffDock fold the receptor with ESMFold first; that path and a larger-library screening recipe are in references/workflows.md.

Under --out_dir/<complex_name>/ each sample is written as rank{N}_confidence{score}.sdf, plus a copy of rank1.sdf for convenience. The confidence value in the filename is a logit, so it is unbounded and can be negative; among samples for the same complex higher is better, but values are not comparable across different complexes or ligands.

The YAML config overwrites your CLI flags

inference.py loads --config default_inference_args.yaml after argparse and replaces every key it finds, so passing --samples_per_complex 40 or --model_dir ... on the command line is silently ignored if the same key sits in the YAML. To change sampling depth or any other key the YAML defines, copy the YAML, edit the copy, and point --config at it.