# Autodock Vina > Structure-based docking with AutoDock Vina, Vinardo, and AutoDock4 through the Meeko toolchain. Use this skill to define a docking box, prepare receptors and ligands as PDBQT, run single or batch docking, rescore, and interpret affinities, poses, and ligand efficiency. Covers box definition from a reference ligand or pocket residues, protonation and tautomer decisions, flexible side chains, exhaustiveness and seeds, redocking validation, and virtual screening over compound libraries. Also trigger on vina, smina, gnina, mk_prepare_ligand, mk_prepare_receptor, mk_export, scrub.py, PDBQT, autogrid4, docking box, or binding-pose prediction. - Skill: `k-dense-ai/autodock-vina` (Agent Skill, multi-file: 8 files) - Install (CLI): `npx skillmds@latest add k-dense-ai/autodock-vina` - Raw SKILL.md: https://api.skillmd.com/api/skills/k-dense-ai/autodock-vina/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - License: MIT - Author: K-Dense AI (https://skillmd.com/u/k-dense-ai) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/k-dense-ai/autodock-vina --- # AutoDock Vina Classical, CPU-only, physics-style docking: put a ligand in a defined box and search for the pose that minimises an empirical scoring function. Unlike `diffdock`, it returns a score you can rank with; unlike `boltz`, it needs a receptor structure and a defined site, and runs on a laptop. **Docs:** [autodock-vina.readthedocs.io](https://autodock-vina.readthedocs.io) · [meeko.readthedocs.io](https://meeko.readthedocs.io) **Checked against:** Vina 1.2.7, Meeko 0.7.1. Read [references/receptor-preparation.md](references/receptor-preparation.md) and [references/ligand-preparation.md](references/ligand-preparation.md) before running anything — that is where accuracy is won. Read [references/scoring-and-interpretation.md](references/scoring-and-interpretation.md) before reporting a number, and [references/troubleshooting.md](references/troubleshooting.md) when something fails. ## Before anything else: what the score is Vina's "affinity" in kcal/mol is an empirical scoring function with roughly **2–3 kcal/mol error** — about two orders of magnitude in Kd. It is useful for enriching a library and for predicting a pose. It is not a predicted binding free energy, it is not comparable across targets or across scoring functions, and a −9.5 and a −8.2 are not distinguishable. Report it as what it is. It also **scales with heavy-atom count**, so a library ranked by raw score puts the biggest molecules on top. Rank with ligand efficiency alongside; the parser computes it. ## The workflow ```bash # 1. box, from the co-crystal ligand of a holo structure python skills/autodock-vina/scripts/make_box.py 1iep.cif \ --reference-ligand STI --out box.txt --box-pdb box.pdb # 2. receptor and ligand PDBQT (Meeko, external) mk_prepare_receptor.py -i receptor_H.pdb -o receptor -p -v \ --box_center 15.190 53.903 16.917 --box_size 20 20 20 scrub.py ligands.smi -o ligands_3d.sdf --ph 7.4 # 3. dock python skills/autodock-vina/scripts/dock_batch.py run \ --receptor receptor.pdbqt --config box.txt --ligands ligands_3d.sdf \ --exhaustiveness 32 --seed 42 --workers 8 --out-dir docking/ # 4. read the results, with the sanity checks python skills/autodock-vina/scripts/parse_vina_output.py docking/*_out.pdbqt \ --config box.txt --summary ``` `dock_batch.py check` verifies the toolchain first; `--dry-run` prints every command without running it. ## The box is the parameter that matters Too small and the correct pose cannot fit. Too large and the search dilutes — the exhaustiveness budget is fixed, so doubling the volume halves the sampling density and quietly degrades every result. ```bash # see what is bound before choosing python skills/autodock-vina/scripts/make_box.py 1iep.cif --list-ligands # component chain resseq atoms # STI A 201 37 python skills/autodock-vina/scripts/make_box.py 1iep.cif --reference-ligand STI # center_x = 15.190 size_x = 18.664 # center_y = 53.903 size_y = 26.739 # center_z = 16.917 size_z = 23.526 ``` Four ways to define it, in descending order of reliability: `--reference-ligand` (a bound ligand in a holo structure), `--residues A:790,A:797,A:855` (known pocket residues), `--center/--size` (explicit), and `--chain` (blind docking, which rarely reproduces a known pose — the script warns). Ligand auto-selection skips waters, ions, buffers, and cryoprotectants, so the box does not land on a sulfate. Write `--box-pdb` and load it next to the receptor in PyMOL; looking at the box takes ten seconds and catches the coordinate mix-ups that produce a whole campaign of nonsense. ## Reading results, including the failure flags ```bash python skills/autodock-vina/scripts/parse_vina_output.py out.pdbqt --config box.txt ``` ``` ligand rank affinity_kcal_mol ligandEfficiency heavyAtoms rmsd_lb atEdge lig1 1 -12.5 -0.34 37 0.000 lig1 2 -12.2 -0.33 37 1.234 +x lig1 3 -9.1 -0.25 37 3.456 # warning: pose atoms within 1 A of the box wall (+x) -- the search was clipped # warning: best and second pose differ by only 0.30 kcal/mol ``` - **`atEdge` invalidates a score.** A pose touching the wall means the optimum may lie outside the box. Enlarge or recentre and re-dock. Passing `--config` is what enables this check, and it is the reason to pass it. - **A sub-0.5 kcal/mol gap** between the top two poses means the ranking is not a discrimination. - `rmsd_lb`/`rmsd_ub` are measured from the best pose, so a large spread means several distinct binding modes and a small one means the search kept converging — that is a good sign. ## Settings that are not the defaults - **`--exhaustiveness 32`, not 8.** The AutoDock documentation says so itself for the imatinib tutorial. The default was tuned for small rigid ligands in a tight box. - **`--seed`.** The search is stochastic; without a fixed seed the run is not reproducible, and a ranking that changes between seeds is not a ranking. - **`--scoring vinardo`** is worth trying when Vina's poses look wrong. Scores from different functions are *not comparable with each other*. ## Validate before you trust Redock the co-crystal ligand into its own structure and measure RMSD to the crystal pose. Under 2 Å means the box, the protonation, and the receptor preparation can reproduce a known answer. Above that, fix the setup before docking anything unknown. Then cross-dock ligands from other structures of the same target — that predicts screening performance far better than self-docking. One run, one hour, and it is the only calibration the method offers. ## Where this fails Metalloenzymes, highly charged pockets, water-mediated binding, induced fit needing backbone movement, covalent inhibitors, ligands with more than ~15 rotatable bonds, and fragments. In those cases say the method does not apply rather than reporting a score anyway. [references/troubleshooting.md](references/troubleshooting.md) covers each and names the alternatives (smina, gnina, AutoDock-GPU, covalent protocols). ## Composing with the rest of the bundle - `uniprot-rcsb` → here: find and check the structure, confirm the site residues are actually resolved, and download the coordinates. - `binding-site-analysis` → before: is the pocket worth docking into at all, and where exactly is it? `pocket_box.py --format vina` writes this skill's box config directly. - `chemical-space` → before: purchasable compounds to dock, and a costed screening cascade. - `free-energy-perturbation` → after: rigorous ΔΔG on the tens of compounds worth it. - `medchem` / `rdkit` / `datamol` → here: triage and standardise the library first. Docking 20,000 PAINS wastes the compute and pollutes the hit list. - `molecular-dynamics` → after: run the top poses; a pose that leaves the site in 10 ns was not a pose. - `boltz` → alongside: a trained affinity head answers a different question from a physics-style score, and agreement between the two is worth more than either alone. - `diffdock` → alternative: diffusion-based pose generation with no box, but no affinity. - `chembl` → validation: known actives against your target, for decoy enrichment.