# Ensemble docking Dock a ligand across multiple prepared receptor conformations and retain state-specific poses for comparison. [Start this workflow](https://proteiniq.io/app/workflows/templates?template=ensemble-docking)[Contact sales](https://proteiniq.io/contact) ## More molecular docking workflows ## Tools for ensemble docking Use these methods to prepare inputs, run the core analysis, inspect outputs, and validate the evidence described in this workflow. [![AlphaFlow](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Falphaflow.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### AlphaFlow Generate a protein conformational ensemble](https://proteiniq.io/app/alphaflow)[![MDGen](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fmdgen.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### MDGen Sample alternative structural states](https://proteiniq.io/app/mdgen)[![OpenMM](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fopenmm.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### OpenMM Generate or relax receptor conformations](https://proteiniq.io/app/openmm)[![GROMACS](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fgmx-mmpbsa.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### GROMACS Run molecular-dynamics ensemble generation](https://proteiniq.io/app/gromacs)[![USAlign](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fusalign.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### USAlign Measure ensemble structural diversity](https://proteiniq.io/app/usalign)[![fpocket](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Ffpocket.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### fpocket Compare pocket geometry across states](https://proteiniq.io/app/fpocket)[![AutoDock Vina](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fautodock-vina-announcement.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### AutoDock Vina Dock each receptor conformation](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 Add CNN-scored ensemble poses](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 Generate poses per conformation](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 Compare ligand-induced conformations](https://proteiniq.io/app/dynamic-bind)[![PoseBusters](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fposebusters.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### PoseBusters Check selected poses](https://proteiniq.io/app/posebusters)[![ProLIF](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fprolif.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### ProLIF Compare contacts across states](https://proteiniq.io/app/prolif) ## What is ensemble docking? Ensemble docking is a [molecular docking](https://proteiniq.io/use-cases/molecular-docking) method for testing a ligand against several receptor conformations rather than one fixed protein structure. Each receptor state is docked separately, so the result retains both a ligand pose and the protein conformation that permitted it. The conformations may come from experimental structures, alternate models or selected simulation snapshots. They represent discrete possibilities for a flexible pocket. Unlike [flexible molecular docking](https://proteiniq.io/use-cases/flexible-molecular-docking), a docking run does not move the receptor continuously to fit the ligand. An ensemble can recover a pose blocked in one structure. It can also create more chances for a spurious high rank, especially when a poorly prepared conformation forms an artificial cavity. Scores from different receptor states are conditional on their geometry and should not be treated as one calibrated energy scale. ### When to use ensemble docking An ensemble is useful when there is evidence for more than one relevant receptor state: - Experimental structures show alternative pocket shapes. - A side-chain rotamer controls access to the site. - A validated simulation samples distinct pocket conformations. - A ligand class docks poorly to one receptor state but fits another. ### Strengths and limitations The method makes receptor-state sensitivity visible and can find poses that a single rigid structure excludes. Because each run is independent, it is straightforward to trace a result back to the conformation that produced it. Ensemble quality depends on state selection. Redundant conformations add little information, while distorted or irrelevant states can dominate the apparent hit list. Docking across states does not establish their population in solution or the energetic cost of reaching them. ## How ensemble docking works First curate a small set of structurally distinct, biologically plausible receptors. Prepare chains, cofactors, protonation and atom naming consistently. Dock the same ligand state into each receptor, then compare both the resulting pose and the state that supported it. The featured workflow runs [DiffDock-L](https://proteiniq.io/app/diff-dock) independently against three supplied receptor conformations. Its confidence ranks poses within a model run; it is not a free energy for comparing receptor populations. A classical ensemble can instead run [AutoDock Vina](https://proteiniq.io/app/autodock-vina) in the same pocket box for each state, but that is a separate protocol with its own scoring assumptions. ## Applications of ensemble docking Kinase activation states, ligand-gated loops and alternate side-chain rotamers are natural ensemble questions. A pose that recurs in several credible structures may be less sensitive to one coordinate set. A pose that appears in only one state can still be useful if independent evidence supports that state. For virtual screening, multiple receptor states can broaden the range of chemotypes considered. Interpret a compound's best result with the state identity attached. Taking the best score across a large ensemble without accounting for the number and quality of states favors chance extremes. ## How to do ensemble docking online 1. **Choose receptor states.** Select three distinct structures relevant to the binding question, retaining their source and biological state. 2. **Prepare them consistently.** Align the proteins, inspect missing atoms and cofactors, and use [PDBFixer](https://proteiniq.io/app/pdb-fixer) for repairs that can be justified across states. 3. **Standardize the ligand.** Use one defined chemical state and stereoisomer for the comparison. 4. **Run the ensemble workflow.** Connect each receptor PDB and the shared ligand to its own DiffDock-L node. Keep each node's output associated with its input receptor. 5. **Check pose geometry.** Inspect each state-specific complex and use [PoseBusters](https://proteiniq.io/app/posebusters) for ligand and receptor-clash checks. 6. **Compare states.** Group poses by pocket and orientation, then report which receptor conformations support each hypothesis. ## How to interpret ensemble docking results Look for a chemically valid pose in a credible receptor state before comparing confidence. [DiffDock-L confidence](https://proteiniq.io/app/diff-dock#how-to-interpret-diffdock-confidence-scores) concerns pose accuracy under its model; it does not measure binding energy or state population. A successful result in one unusual conformation is weaker evidence when the state itself lacks support. Report every state tested and avoid hiding failures behind the best pose. Ensemble docking can prioritize receptor states and ligand poses for follow-up, but it does not confirm binding, activity, safety or function. ## How the ensemble docking workflow works Run DiffDock-L independently against three supplied receptor conformations and preserve every result by conformation. 1. **Assemble receptor conformations.** Choose structures that represent meaningful pocket states, and record the source, construct, ligands, mutations, resolution, and preparation history of each one. 2. **Normalize preparation.** Align structures, standardize chains and residues, repair comparable missing atoms, and apply consistent protonation and cofactor decisions. 3. **Control ensemble redundancy.** Cluster by pocket geometry or structural similarity and retain representative conformations. Exclude states with preparation artifacts or collapsed pockets. 4. **Dock each state.** Open the ensemble workflow, supply three receptor conformations and one ligand, and run [DiffDock-L](https://proteiniq.io/app/diff-dock) separately for each state. 5. **Compare state-specific results.** Review poses, confidence, contacts, and pocket geometry by receptor state. Do not silently pool raw scores across conformations as if they shared one calibrated scale. ## Inputs and outputs Check formats before running, then inspect and download the result from every workflow step. ### Inputs #### Structural inputs `PDB``SDF``SMILES` Two or more consistently prepared receptor PDB conformations plus a ligand. #### Method context Binding-site evidence, restraints, receptor-state provenance, known ligands, or reference complexes when available. ### Outputs #### Docked structures `PDB``PDBQT``SDF` Per-conformation poses, confidence or scores, receptor provenance, and comparison-ready structure files. #### Review evidence Method-native rankings, confidence, logs, interaction context, failures, and files for reproducible follow-up. ## 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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