# Structure-based virtual screening

Prepare a target structure, review a compound library, generate ranked poses with GNINA, and inspect pose-quality and ADMET results in one editable workflow.

[Run this workflow](https://proteiniq.io/app/workflows/templates?template=structure-based-virtual-screening)[Contact sales](https://proteiniq.io/contact)

## More virtual screening workflows

## Tools for structure-based virtual screening

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

[![PDBFixer](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fpdb-fixer-featured.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### PDBFixer Repairs missing structural detail and records preparation settings.](https://proteiniq.io/app/pdb-fixer)[![fpocket](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Ffpocket.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### fpocket Detects pockets and reports pocket-level geometry and druggability metrics.](https://proteiniq.io/app/fpocket)[![PAINS filter](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fpains-filter.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### PAINS filter Flags PAINS substructures for compound-library review.](https://proteiniq.io/app/pains-filter)[![Brenk filter](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fbrenk-filter.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### Brenk filter Reports potentially problematic structural features in screening compounds.](https://proteiniq.io/app/brenk-filter)[![Lipinski's rule of 5](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Flipinskis-rule-of-5.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### Lipinski's rule of 5 Calculates rule-of-five properties and pass or fail results per compound.](https://proteiniq.io/app/lipinskis-rule-of-5)[![Lead-likeness filter](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Flead-likeness-filter.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### Lead-likeness filter Reviews compound properties against the tool’s lead-likeness criteria.](https://proteiniq.io/app/lead-likeness-filter)[![GNINA](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fgnina.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### GNINA Generates ranked docking poses with CNN and Vina scoring outputs.](https://proteiniq.io/app/gnina)[![AutoDock Vina](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fautodock-vina-announcement.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### AutoDock Vina Docks compounds with configurable Vina-family scoring and search settings.](https://proteiniq.io/app/autodock-vina)[![DiffDock-L](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fdiffdock-featured-image.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### DiffDock-L Predicts protein–ligand poses with a diffusion-based docking model.](https://proteiniq.io/app/diff-dock)[![PoseBusters](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fposebusters.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### PoseBusters Checks docked poses for geometric and chemical plausibility.](https://proteiniq.io/app/posebusters)[![ADMET-AI](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fadmet-ai.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### ADMET-AI Predicts physicochemical, ADMET, toxicity, alert, and percentile endpoints.](https://proteiniq.io/app/admet-ai)[![Admetica](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fadmetica.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### Admetica Provides an independent set of predicted ADMET properties for triage.](https://proteiniq.io/app/admetica)

## What is structure-based virtual screening?

Structure-based virtual screening (SBVS) is a computational method for evaluating a compound library against the three-dimensional structure of a biological target, usually a protein. Docking algorithms sample candidate poses in a defined search region, and scoring functions rank those poses for review. The output is a target-specific shortlist and binding hypotheses, not measured affinity or confirmed activity.

SBVS differs from ligand-based screening because it can begin without known active ligands, but it requires a defensible receptor conformation and binding-site model. Target preparation, protonation, retained waters or cofactors, search-box placement, ligand states, sampling effort, and score interpretation can all change which compounds rise to the top.

Most campaigns use rigid-receptor docking for throughput, [ensemble docking](https://proteiniq.io/use-cases/ensemble-docking) to represent several receptor states, or a staged design that rescales a focused subset with complementary methods. Docking scores are most defensible within one consistent setup. Pose plausibility, interactions, controls, diversity, and experimental follow-up remain separate evidence.

### When to use structure-based virtual screening

Use a target structure when its binding site can be defined and checked:

- Start from an experimental structure or a model that has been reviewed for the binding site and conformational state relevant to the screen.
- Use docking when candidate binding geometry and target interactions matter, not only molecular similarity to known ligands.
- Keep preparation settings, ranked poses, score tables, pose checks, predictive endpoints, and files together for review and handoff.

### Strengths and limitations

A computational ranking can narrow a large library to a smaller set for closer review and physical testing. Predicted poses provide a three-dimensional hypothesis for how a compound may fit within a binding site and interact with nearby residues. Using consistent preparation, docking, and scoring settings makes it easier to compare candidates within the same screening run. Ranked poses can be inspected alongside compound properties, pose checks, and predictive endpoints before selecting compounds for assays.

Missing atoms, incorrect protonation, an unsuitable conformational state, or an inaccurate predicted structure can distort the binding site and the resulting poses. Automatically detected pockets are hypotheses. A poorly supported docking region can produce plausible-looking rankings that are not relevant to the biological mechanism. Docking scores simplify solvation, entropy, receptor flexibility, and other physical effects. They can produce false positives and false negatives and should not be read as measured affinity. A single receptor structure captures one conformational state, while real targets may adopt multiple states or reorganize when a ligand binds. A computational shortlist identifies candidates for follow-up. Binding, activity, selectivity, safety, and efficacy still require appropriate experiments.

## Molecular docking and virtual screening

[Molecular docking](https://proteiniq.io/use-cases/molecular-docking) is the calculation at the center of many structure-based virtual screening protocols: it generates candidate protein–ligand poses and assigns method-specific scores. Docking-based virtual screening repeats that calculation across a compound library under a consistent receptor, search-space, sampling, and scoring setup.

The terms are related but not interchangeable. One docking run may examine a single ligand, while a docking virtual screening campaign also includes target and library preparation, failure handling, ranking, pose review, shortlist selection, and validation. Tools such as [GNINA](https://proteiniq.io/app/gnina), [AutoDock Vina](https://proteiniq.io/app/autodock-vina), and [SMINA](https://proteiniq.io/app/smina) support the docking stage; their scores should remain in the context of the engine and setup that produced them.

## Core structure-based screening approaches

The receptor model and sampling strategy should match the biological question. Adding complexity is useful only when validation shows that it improves recovery or pose quality.

### Focused rigid-receptor docking

Screen one prepared receptor state in a supported pocket when throughput and a consistent search space matter most.

### Ensemble docking

Run compounds against several justified receptor conformations and keep conformation-specific poses and scores visible.

### Consensus review

Compare complementary docking or rescoring methods without averaging unrelated raw scores unless the combination rule has been validated.

## Structure-based virtual screening applications

SBVS is useful when the target structure contributes information that a ligand-only comparison cannot provide. Common applications include focused hit discovery in a known pocket, analog prioritization, allosteric-site exploration, and selecting compounds for experimental counter-screens.

The method is weakest when the relevant receptor state or pocket is unknown, the site depends strongly on induced fit, or structures omit essential partners such as cofactors, membranes, metals, or ordered waters. In those cases, the campaign should narrow its claim or compare several explicitly justified setups.

## How to do structure-based virtual screening online

Pilot the complete protocol on a small validation set before committing the full library. This catches preparation and pocket errors while they are still cheap to correct.

1. **Select the receptor state.** Choose an experimental structure or reviewed model whose chains, ligands, cofactors, and conformation match the intended binding question.
2. **Define and prepare the pocket.** Repair the receptor with [PDBFixer](https://proteiniq.io/app/pdb-fixer) where needed, then compare the chosen site with [fpocket](https://proteiniq.io/app/fpocket) cavities. Document protonation and retained components; a cavity prediction is not binding evidence.
3. **Standardize the ligand library.** Preserve identifiers while resolving salts, stereochemistry, protonation, tautomers, duplicates and invalid structures. The template's Lipinski and lead-likeness filters should be treated as triage rules, not activity predictions.
4. **Validate docking settings.** Test search-space placement, sampling effort, and scoring on known ligands, decoys, or pose-recovery examples when suitable evidence exists.
5. **Run and inspect the screen.** Dock with GNINA under one reproducible pocket setup. Check its default need for a positioned reference ligand. Inspect pose geometry with [PoseBusters](https://proteiniq.io/app/posebusters) and residue contacts with [ProLIF](https://proteiniq.io/app/prolif), keeping their evidence separate from the docking score.
6. **Select a diverse shortlist.** Balance rank, pose plausibility, chemical diversity, liabilities, and assay feasibility before orthogonal calculations and experiments.

## How to interpret structure-based virtual screening results

Compare GNINA ranks within a consistent receptor, pocket and ligand-preparation protocol. Its empirical energies and CNN-derived scores are different outputs; neither is a measured binding constant. A compound that passes a property filter or a pose check has cleared one specific screen, not a biological validation step.

Report how many compounds failed preparation or docking and retain alternative poses for close candidates. A diverse shortlist with plausible geometry and control recovery can guide experiments, but virtual screening does not confirm binding, activity, safety or function.

## How structure-based virtual screening works

SBVS usually combines target preparation, compound-library review, molecular docking, scoring, pose-quality checks, and shortlist selection. Each stage affects the reliability of the final ranking, so its settings and outputs should remain available for review.

1. **Prepare the receptor with PDBFixer and fpocket.** Repair the receptor with PDBFixer, then inspect [fpocket](https://proteiniq.io/app/fpocket) binding-site context before interpreting docking results.
2. **Review the library with Lipinski and lead-likeness filters.** Keep [Lipinski](https://proteiniq.io/app/lipinskis-rule-of-5)’s rule of 5 and Lead-likeness filter results with compound identifiers and any failed records.
3. **Dock compounds with GNINA.** Generate ranked [GNINA](https://proteiniq.io/app/gnina) poses and retain each engine-specific score in the configured receptor context.
4. **Inspect poses with PoseBusters and ProLIF.** Review [PoseBusters](https://proteiniq.io/app/posebusters) geometry checks and ProLIF interaction fingerprints alongside individual docked poses.
5. **Review ADMET-AI context and export a shortlist.** Combine ADMET-AI context, failures, chemical diversity, and pose review before exporting a shortlist for validation.

## Inputs and outputs

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

### Inputs

#### Target structure

`PDB`

Provide a reviewed receptor structure and retain preparation decisions for the binding-site context.

#### Compound library

`SMILES`

Submit a standardized library with stable compound identifiers and documented input exclusions.

#### Binding-site context

Use known pocket coordinates or a reference ligand when available to guide review of the docking setup.

### Outputs

#### Prepared target and pocket results

`PDB``JSON`

Download the fixed receptor, fpocket files, and reported pocket metrics.

#### Library-review tables

`CSV``JSON`

Inspect retained Lipinski and lead-likeness results per compound.

#### Docking poses and scores

`SDF``CSV``JSON`

Review GNINA poses with their native CNN and Vina-family scoring outputs.

#### Pose and ADMET review

`CSV``JSON`

Keep PoseBusters, ProLIF, and ADMET-AI output connected to the supporting evidence.

## Frequently asked questions

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

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