Docking: AutoDock Vina
A single-complex molecular docking lab built on AutoDock Vina 1.2.7. Drop in a prepared receptor and ligand as PDBQT files, get back ranked poses with binding affinities and a merged top-ranked complex.
What it simulates
- Classic CPU-backed AutoDock Vina docking against a configurable search box.
- Pinned official Vina 1.2.7 release in managed runtime mode (binaries cached after first run).
- Bundled
1iepcomplex so a fresh run produces real poses without setup. - Ranked pose table, structural artifacts, and full Vina stdout/stderr.
Run it on the Hub
- Open the AutoDock Vina: VinaDockingPredictor Lab on the public Hub.
- Click Run. The bundled
1iepdefaults dock without any parameter editing.
Inputs you can tune
| Input | Meaning |
|---|---|
receptor_pdbqt_path | Prepared receptor PDBQT file. Defaults to data/1iep/1iep_receptor.pdbqt. |
ligand_pdbqt_path | Prepared ligand PDBQT file. Defaults to data/1iep/1iep_ligand.pdbqt. |
run_options.box_center | Search-box center (Å, in receptor coordinates). |
run_options.box_size | Search-box edge lengths (Å). |
run_options.exhaustiveness | Vina sampling effort. Higher is slower and more thorough. |
run_options.n_poses | Number of poses to return. |
run_options.scoring | Vina scoring function (vina, vinardo, ad4). |
What results to expect
- Pose ranking table: each pose with binding affinity (kcal/mol, lower is stronger) plus lower- and upper-bound RMSD. The bundled
1ieprun reports five poses with a top affinity around -13.3 kcal/mol. - 3D structure view: receptor with the top-ranked pose merged in as
top_rank_complex.pdb. Use it to confirm the ligand sits inside the search box and a plausible pocket. - Docking summary: search box, exhaustiveness, scoring function, and pose count.
- Run metadata: Vina version, runtime cache directory, truncated stdout/stderr, and
status: ok/status: error.
The lab exposes generated PDBQT, PDB, and JSON files through structure_artifacts, so remote runs upload them as durable run artifacts before completion.
Source on GitHub: models-autodock-vina. See Artifact Outputs for the file-output contract. For machine-learning-based docking, see the DiffDock-L example.