diffdock
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.
npx skills add BioTender-max/awesome-bio-agent-skills --skill diffdock --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 ```bash 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
- Running it
- The YAML config overwrites your CLI flags
- The first run is silent for ~11 minutes and needs ≥32 GB host RAM
- The README's --ligand works on the CLI by accident — use --liganddescription
- Errors worth recognizing
cd $DIFFDOCK_REPO # a clone of github.com/gcorso/DiffDock python3 -m inference \
What does the diffdock skill do?
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.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill diffdock --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.