| name | diffdock |
| description | Predict small-molecule binding poses with DiffDock-L (Corso et al. 2023/2024, github.com/gcorso/DiffDock) — blind diffusion docking that places a ligand into a protein pocket without a predefined search box and ranks the samples with a learned confidence model. Reach for this skill to dock a SMILES or SDF against a PDB, to generate ranked 3D poses for a small fragment library, or to get a starting pose for downstream rescoring. DiffDock predicts geometry, not affinity.
|
| license | Apache-2.0 |
| origin | openai4s |
| category | biomodels |
| requirements | ["gpu"] |
| capabilities | {"network":{"mode":"raw_required","domains":[]}} |
| metadata | {"display-name":"DiffDock","third_party":[{"kind":"weights","name":"DiffDock-L","license":"MIT","terms_url":"https://github.com/gcorso/DiffDock/blob/main/LICENSE"}]} |
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
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.
The first run is silent for ~11 minutes and needs ≥32 GB host RAM
