# 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 `1iep` complex so a fresh run produces real poses without setup. - Ranked pose table, structural artifacts, and full Vina stdout/stderr.  ## Run it on the Hub 1. Open the [AutoDock Vina: VinaDockingPredictor Lab](https://hub.biosimulant.com/labs/13445afd-7ed7-4981-977b-92156423dd19) on the public Hub. 2. Click **Run**. The bundled `1iep` defaults 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 `1iep` run 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.  **Info:** Source on GitHub: [models-autodock-vina](https://github.com/Biosimulant/models-autodock-vina). See [Artifact Outputs](/references/library/artifact-outputs) for the file-output contract. For machine-learning-based docking, see the DiffDock-L example.