# Molecular docking

Dock a ligand into a protein online with AutoDock Vina, GNINA or DiffDock-L, or dock two proteins with LightDock, then check every pose before you trust its score.

[Run AutoDock Vina](https://proteiniq.io/app/autodock-vina)[Compare docking methods](https://proteiniq.io/app/workflows/templates?template=molecular-docking-method-panel)[Contact sales](https://proteiniq.io/contact)

## More molecular docking workflows

## Tools for molecular docking

Use these methods to prepare inputs, run the core analysis, inspect outputs, and validate the evidence described in this workflow.

[![AutoDock Vina](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fautodock-vina-announcement.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### AutoDock Vina Classical protein–ligand docking](https://proteiniq.io/app/autodock-vina)[![GNINA](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fgnina.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### GNINA CNN-assisted protein–ligand docking](https://proteiniq.io/app/gnina)[![DiffDock-L](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fdiffdock-featured-image.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### DiffDock-L Diffusion protein–ligand docking](https://proteiniq.io/app/diff-dock)[![DynamicBind](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fdynamic-bind.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### DynamicBind Dynamic protein–ligand docking](https://proteiniq.io/app/dynamic-bind)[![LightDock](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Flightdock.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### LightDock Macromolecular and peptide docking](https://proteiniq.io/app/lightdock)[![DFMDock](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fdfmdock.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### DFMDock Learned protein–protein docking](https://proteiniq.io/app/dfmdock)[![FlowDock](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fflowdock.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### FlowDock Flow-based complex prediction](https://proteiniq.io/app/flowdock)[![fpocket](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Ffpocket.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### fpocket Binding-pocket detection](https://proteiniq.io/app/fpocket)[![PDBFixer](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fpdb-fixer-featured.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### PDBFixer Structure preparation](https://proteiniq.io/app/pdb-fixer)[![PoseBusters](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fposebusters.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### PoseBusters Pose plausibility checks](https://proteiniq.io/app/posebusters)[![ProLIF](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fprolif.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### ProLIF Interaction fingerprints](https://proteiniq.io/app/prolif)[![DockQ](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fdockq.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### DockQ Native-reference complex assessment](https://proteiniq.io/app/dockq)

## What is molecular docking?

Molecular docking is a computational method for generating and ranking candidate bound arrangements of two molecular partners. The partners may be a small molecule and a protein, two proteins or a peptide and a receptor. A docking result is a structural hypothesis, not a direct observation of binding.

Every docking calculation combines a search with a way to rank the structures it finds. The search may move a ligand through a defined pocket, survey an entire protein surface or adjust both partners. Scoring may use approximate interaction energies, learned patterns or model confidence, each with a different meaning.

The right method follows the molecular system and the evidence available. A small-molecule engine cannot simply substitute for a protein interface method, and a good score cannot compensate for a wrong protonation state, an inaccessible surface or an omitted binding partner.

### When to use molecular docking

Docking is useful when a concrete structural question can guide preparation and review:

- Propose contacts for an interaction supported by other evidence.
- Compare plausible orientations in a known site or interface.
- Test sensitivity to receptor state or docking method.
- Choose models for mutagenesis, simulation or structural follow-up.

### Strengths and limitations

Docking returns explicit three-dimensional complexes that can be inspected and challenged. It can show which residues a ligand might contact, where alternative orientations arise and how a conclusion changes with a different receptor state or search method.

The search samples only part of a large conformational space. Preparation and biological context can dominate the answer, while fast scoring functions omit important solvent and conformational effects. Even a chemically plausible, high-ranked pose does not establish affinity, occupancy or function.

## Which molecular docking tool to use

Two things decide the tool: what the partners are, and whether you already know where they bind. Every tool below runs online from an uploaded PDB, SDF or SMILES file and quotes its credit cost before it starts. An AutoDock Vina run starts at 15 credits.

| What you want to do | Tool | What you get back |
| --- | --- | --- |
| Dock a small molecule into a known pocket | [AutoDock Vina](https://proteiniq.io/app/autodock-vina) | Ranked poses with a Vina score in kcal/mol |
| Dock into a known pocket and rescore with a neural network | [GNINA](https://proteiniq.io/app/gnina) | Ranked poses with CNN and empirical scores |
| Dock a ligand when the pocket is unknown | [DiffDock-L](https://proteiniq.io/app/diff-dock) | Ranked poses with learned confidence, no box needed |
| Let the pocket move as the ligand binds | [DynamicBind](https://proteiniq.io/app/dynamic-bind) | Poses with a ligand-adapted protein conformation |
| Find candidate pockets before docking | [fpocket](https://proteiniq.io/app/fpocket) | Pockets ranked by druggability, with geometric descriptors |
| Dock two proteins or a protein and a peptide | [LightDock](https://proteiniq.io/app/lightdock) | Ranked complex models from a swarm search |
| Dock two proteins with a learned model | [DFMDock](https://proteiniq.io/app/dfmdock) | Diffusion-sampled complexes ranked by learned energy |
| Check that a pose is physically sensible | [PoseBusters](https://proteiniq.io/app/posebusters) | Pass or fail on bond geometry, clashes and stereochemistry |
| List the contacts in a pose | [ProLIF](https://proteiniq.io/app/prolif) | Residue-level interaction fingerprints |

AutoDock Vina is the usual starting point for a small molecule in a known site, because its scores are widely reported and easy to compare with published work. DiffDock-L suits a ligand with no defined pocket, but its confidence ranks its own poses and is not a binding energy. Whichever engine you use, run [PDBFixer](https://proteiniq.io/app/pdb-fixer) on the receptor first if the structure has missing atoms or residues.

## What a molecular docking calculation actually does

The inputs define which molecules and conformations exist in the calculation. The search then proposes relative placements and, where supported, changes in ligand torsions or protein geometry. The ranking model orders those proposals. A run can fail by missing the right configuration, scoring it poorly or starting from a structure in which that configuration is inaccessible.

The model family determines what its numbers mean. [AutoDock Vina](https://proteiniq.io/app/autodock-vina) reports an approximate docking energy in kcal/mol. [GNINA](https://proteiniq.io/app/gnina) adds convolutional-network scores. [DiffDock-L](https://proteiniq.io/app/diff-dock) returns [learned pose confidence](https://proteiniq.io/app/diff-dock#how-to-interpret-diffdock-confidence-scores). These numbers answer different questions and should remain attached to their native methods.

## How to choose a molecular docking method

First identify the partners. Protein–ligand docking handles a small molecule in a protein site, and is the case the tools above are built for. [Protein–protein docking](https://proteiniq.io/use-cases/protein-protein-docking) addresses broad macromolecular interfaces. [Antibody–antigen docking](https://proteiniq.io/use-cases/antibody-antigen-docking) adds paratope and epitope context, while [peptide–protein docking](https://proteiniq.io/use-cases/peptide-protein-docking) must contend with a flexible peptide backbone.

Then decide what is uncertain. [Blind docking](https://proteiniq.io/use-cases/blind-docking) searches when the site is unknown. [Flexible molecular docking](https://proteiniq.io/use-cases/flexible-molecular-docking) permits receptor motion during pose prediction, whereas [ensemble docking](https://proteiniq.io/use-cases/ensemble-docking) tests several prepared receptor states independently. [Consensus docking](https://proteiniq.io/use-cases/consensus-docking) compares methods under a stated agreement rule. [AI molecular docking](https://proteiniq.io/use-cases/ai-molecular-docking) uses learned generative or hybrid models; its confidence remains model-specific.

| Question | Starting approach | Main boundary |
| --- | --- | --- |
| Small molecule, supported pocket | Local protein–ligand docking | The defined search region excludes other sites |
| Unknown site | Blind docking | Broad search increases false positives |
| Two macromolecular partners | Partner-specific complex docking | Interface and conformational uncertainty dominate |
| Several credible receptor states | Ensemble docking | Scores do not measure state populations |
| Uncertain scoring or search | Consensus comparison | Agreement can reflect shared bias |

## Applications of molecular docking

In structure-guided research, a docked complex can suggest which contacts to test by mutation or which compound changes might alter a binding orientation. A redocking control against a known complex tests the protocol's ability to recover an observed pose on that system, though it does not guarantee success for new chemistry.

For poorly characterized interactions, docking can organize sparse evidence into competing models. The most useful result may be a disagreement: two methods favor different interfaces, or a pose appears only in one receptor conformation. Those dependencies help design experiments that distinguish hypotheses.

## How to do molecular docking online

1. **Classify the partners.** Choose the matching ProteinIQ workflow for a small molecule, protein partner, antibody or peptide.
2. **Prepare the structures.** Check chain identity, missing atoms, cofactors, waters, ligand stereochemistry and protonation. [PDBFixer](https://proteiniq.io/app/pdb-fixer) can repair justified structure gaps.
3. **Define the search.** Supply a supported pocket, interface restraints or receptor states before running; record what evidence set those boundaries.
4. **Run the matched method.** The featured method panel compares AutoDock Vina, GNINA, DiffDock-L and [DynamicBind](https://proteiniq.io/app/dynamic-bind) for a protein and small molecule. A macromolecular question needs its partner-specific workflow instead.
5. **Review native outputs.** Keep poses, scores, confidence values and logs by tool, and reject impossible geometry with [PoseBusters](https://proteiniq.io/app/posebusters) where applicable.
6. **Test the hypothesis.** Compare with known structures, mutagenesis or binding measurements according to the question.

## How to interpret molecular docking results

Start with input validity and pose geometry, then examine whether the proposed contacts fit known biology. Compare scores within their own method and preparation protocol. A Vina energy, a CNN score and diffusion confidence cannot be combined into one affinity estimate.

Preserve failed runs and alternative poses alongside the selected model. Docking can prioritize experiments or further calculations, but it does not confirm binding, activity, safety or function.

## How the featured workflow works

A defensible workflow prepares inputs consistently, chooses a scientifically matched search strategy, runs one or more methods, and reviews geometry and uncertainty before validation.

1. **Classify the system.** Identify the partner class and biological question.
2. **Prepare structures.** Repair structures and standardize ligand chemistry.
3. **Define the search.** Set site, restraint, flexibility, or ensemble boundaries.
4. **Generate models.** Run complementary methods and preserve native outputs.
5. **Validate.** Inspect geometry and compare with orthogonal evidence.

## Inputs and outputs for the featured workflow

Check formats before running, then inspect and download the result from every workflow step.

### Inputs

#### Molecular structures

`PDB``SDF``SMILES`

Prepared structures appropriate to the selected partner class.

#### Search evidence

Binding sites, restraints, reference complexes, receptor ensembles, or known interactions when available.

### Outputs

#### Candidate complexes

`PDB``PDBQT``SDF`

Method-native ranked poses or complex models.

#### Review package

Scores, confidence, contacts, logs, failures, provenance, and downloadable files.

## Frequently asked questions

AI answers from our docs. No sign-in needed.

## Run your first analysis today

Start with 200 free credits and no installs. Upgrade when your project needs more compute.

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