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 · meeko.readthedocs.io Checked against: Vina 1.2.7, Meeko 0.7.1.

Read references/receptor-preparation.md and references/ligand-preparation.md before running anything — that is where accuracy is won. Read references/scoring-and-interpretation.md before reporting a number, and 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

# 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.

# 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

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 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.