AutoDock Vina

Dock ligands into protein structures and estimate binding modes, poses, and affinity scores.

Job name

Receptor

Ligand

15 credits

Configure input settings on the left, then click "Submit"orLoad an example (it's free)

(Single mode) HIV-1 Protease—Saquinavir

(Simultaneous co-docking) BRD4 Bromodomain—Fragments

(Batch mode) SARS-CoV-2 Mpro—Antiviral Repurposing Panel

What is AutoDock Vina?

AutoDock Vina is a molecular docking program that predicts how a small molecule binds to a protein by searching through possible binding poses and scoring them with an empirical force field. It is the most widely used open-source docking tool, combining fast search with practical accuracy for routine structure-based drug discovery.

On ProteinIQ, the Vina interface supports the full Vina family workflow rather than just the original 2010 defaults. This includes the standard Vina scoring function, the Vinardo variant optimized for virtual screening, and an AutoDock4 scoring option for metal coordination or legacy workflows.

Applications

Predicting ligand binding modes in a known binding pocket
Ranking a focused compound set against one receptor (virtual screening)
Comparing binding hypotheses after small structural changes to prioritize compounds
Testing whether a proposed binding site can accommodate a ligand with plausible geometry

How to use AutoDock Vina online

ProteinIQ runs AutoDock Vina in the browser with cloud execution, so receptor preparation, ligand submission, and docking outputs are available without local installation or command-line setup.

Inputs

Input	Description
`Protein (Receptor)`	Upload a receptor structure as `.pdb` or `.ent`, or fetch one by 4-character PDB ID such as `1HSG`. Protein atoms are required, and the validator warns when the uploaded file appears to contain nucleic acids or YASARA-specific formatting.
`Ligand`	Provide ligand input as SMILES text, a supported structure file (`.pdbqt`, `.sdf`, `.mol`, `.mol2`, `.smiles`, `.smi`, `.txt`, `.csv`), or fetch by PubChem. One ligand is used in `Single ligand` mode, while `Simultaneous co-docking` accepts up to 5 ligands in one run.
`Vina docking mode`	`Single ligand` runs one docking job, while `Simultaneous co-docking` places multiple ligands in the same search space during one run.
`Job name`	Optional label stored with the run to make repeated docking experiments easier to identify.

Ligand validation blocks metal-containing ligands and disconnected multi-fragment submissions in the standard Vina workflow. Those cases are better suited to GNINA or to a more specialized docking setup.

Settings

Setting	Description
`Scoring function`	`Vina` is the default general-purpose choice. `Vinardo` uses a modified empirical model often favored for virtual screening benchmarks. `AutoDock4` uses the classical AutoDock4 force-field-style scoring and is the required option for hydrated ligand and zinc-specific workflows in this interface.
`Docking mode`	`Dock` performs a full search. `Score only` evaluates the submitted pose without searching. `Local only` refines the input pose locally. `Randomize only` generates randomized ligand placement without full docking output ranking.
`Exhaustiveness`	Search thoroughness from 1 to 64, with a default of 8. Larger values increase runtime but improve the chance of recovering lower-energy poses.
`Number of poses`	Maximum number of docked poses returned, from 1 to 50.
`Energy range (kcal/mol)`	Retains poses within the specified energy window from the best-ranked pose. Larger values preserve more alternative solutions.
`Min RMSD between poses (Å)`	Minimum structural separation between reported poses. Increasing this value reduces near-duplicate outputs.
`Max evaluations`	Caps scoring function evaluations. `0` leaves the choice to Vina's automatic internal heuristic.
`Random seed`	Integer seed for reproducible searches. `0` allows nondeterministic initialization.
`Search mode`	`Auto` builds a search region around the receptor, `Manual` supports explicit center and size fields, and `Autobox` generates a receptor-centered box with configurable padding.
`Center X, Y, Z`	Coordinates of the search-box center in `Manual` mode.
`Size X, Y, Z`	Search-box dimensions in Å. Smaller boxes are faster and usually more reliable when the binding site is already known.
`Auto-box padding (Å)`	Extra padding added around the automatically determined search region.
`Grid spacing (Å)`	Spacing used for affinity map discretization. Smaller spacing increases resolution at higher computational cost.
`Force even voxels`	Rounds the box definition to an even grid count for more controlled map geometry.
`Flexible residues`

Results

Output	Description
`Viewer`	Interactive 3D view of receptor and docked pose geometry
`Data`	Table of scored poses with binding affinity (kcal/mol) and links to output files
`Files`	Downloadable structure files for the generated docking poses

How does AutoDock Vina work?

AutoDock Vina combines an empirical scoring function with stochastic global search and gradient-based local optimization. The ligand is translated, rotated, and flexed inside a predefined search volume; each candidate pose is scored; and promising conformations are refined before final clustering and ranking.

Scoring

The scoring function estimates binding favorability using weighted steric, hydrophobic, hydrogen-bonding, and penalty terms. Vina uses the default empirical model. Vinardo modifies the parameterization for improved enrichment in some screening benchmarks. AutoDock4 uses older force-field-style terms and is required when metal-aware behavior or hydrated ligand workflows are needed.

Search

Vina uses multiple independent runs initialized with random poses. Within each run it alternates broad perturbation steps with local refinement, then collects the best solutions across all searches. The Exhaustiveness parameter controls total independent search effort, while Number of poses and Energy range determine how many alternatives survive to the final report.

Flexible residue docking

Most docking calculations keep the receptor rigid apart from ligand torsions. When Flexible residues are specified, selected receptor side chains are allowed to move during the search. This can recover poses that rigid docking would miss, but increases the search space and runtime substantially. Flexible docking is usually reserved for a few residues with a clear mechanistic rationale for moving.

Interpreting results

The affinity score is most useful as a relative ranking within one consistent experiment. Absolute kcal/mol values should not be treated as direct binding free energies, especially across different targets, protonation states, or receptor preparations.

Affinity range	Typical interpretation
< -10 kcal/mol	Very favorable predicted binding
-7 to -10 kcal/mol	Strong docking score worth follow-up
-5 to -7 kcal/mol	Moderate score that often needs visual inspection
> -5 kcal/mol	Weak or uncertain binding hypothesis

Pose geometry matters as much as score. A slightly worse score with sensible hydrogen bonding, steric fit, and ligand burial is often more credible than the top-ranked pose if that pose shows clashes or unrealistic exposure. For batch docking, comparisons are most meaningful when all ligands were prepared with the same protonation and tautomer assumptions.

Limitations

Rigid receptor approximation: Whole-protein backbone motion is not modeled, so induced-fit effects can still be missed even when flexible residues are enabled.
Approximate scoring: Docking scores support prioritization, not definitive affinity prediction.
Preparation sensitivity: Protonation state, tautomer choice, bound waters, and search-box placement can change rankings substantially.
Metal handling: Standard Vina scoring is not a good default for metal-dependent binding chemistry—use GNINA or AutoDock4 for those cases.
Large or highly flexible ligands: Search quality drops as conformational complexity increases, even when runtime is increased.
Fragment handling: Standard Vina blocks metal-containing ligands and disconnected multi-fragment submissions—use GNINA for those cases.

Related tools

AutoDock-GPU

GPU-accelerated molecular docking using the AutoDock4 force field. Up to 56x faster than serial AutoDock via CUDA parallelization of the Lamarckian Genetic Algorithm.

PandaDock

Open-source molecular docking platform using physics-based scoring functions. CPU-optimized algorithms achieve sub-angstrom accuracy (0.014A RMSD) without GPU requirements.

SMINA

SMINA is a fork of AutoDock Vina with enhanced scoring functions, custom scoring support, and 10-20x faster minimization. Ideal for scoring function development, pose refinement, and high-performance docking workflows.

GNINA

GNINA is a molecular docking tool that combines traditional physics-based docking with deep learning CNN scoring for protein-small-molecule complexes. It provides accurate binding predictions with confidence scores, optimized for high-throughput virtual screening.

DiffDock-L

DiffDock-L is a state-of-the-art molecular docking tool that uses diffusion models to predict how small molecule ligands bind to protein targets. It generates multiple binding poses with confidence scores.

DynamicBind

DynamicBind is an AI-powered protein-ligand binding prediction tool that recovers ligand-induced conformational changes from unbound protein structures. It predicts both ligand binding poses and protein conformational changes.

SigmaDock

SigmaDock is a fragment-based molecular docking tool using SE(3) equivariant diffusion models to predict how small molecule ligands bind to protein targets. Presented at ICLR 2026, it generates multiple binding poses with Vinardo scoring.

SurfDock

SurfDock is a surface-informed diffusion generative model for protein-ligand docking, published in Nature Methods 2024. It leverages protein surface geometry to guide a diffusion process for reliable and accurate protein-ligand complex prediction.

HADDOCK3

HADDOCK (High Ambiguity Driven protein-protein DOCKing) is an integrative modeling platform for biomolecular complexes. It uses experimental data and bioinformatic predictions to guide the docking process, generating accurate protein-protein complex structures.

LightDock

LightDock is a protein-protein, protein-peptide, and protein-DNA docking framework using Glowworm Swarm Optimization (GSO). It predicts macromolecular binding modes and interfaces for biological complexes.