Research-informed screening strategy

Inverse Virtual Screening and Target Fishing Services: Move from a Compound to Plausible Targets

Start with a compound, natural product, metabolite, or phenotypic hit and screen it across a defined target panel. We combine inverse docking with structural, ligand, pathway, and disease-context evidence to produce a prioritized target hypothesis—not an unfiltered list of docking scores.

Discuss your project Choose a screening route

Inverse virtual screening of one compound across a panel of protein targets

Problems this service solves

Turn an Uncertain Search into a Defined Decision

Every project is scoped around the scientific uncertainty, the available evidence, and the number of candidates your team can validate.

A phenotypic hit has no confirmed molecular target

Rank mechanistically plausible targets and define the first experiments needed for confirmation.

A compound shows an unexplained effect or liability

Search for off-target interactions that could explain efficacy, toxicity, or pathway modulation.

A drug or natural product may have a new use

Connect structural compatibility with disease and pathway evidence to focus repurposing hypotheses.

Start from the compound and biological evidence you have

Can We Support Your Inverse Screening Project?

Match the compound, phenotype, species, and target universe to a target-fishing workflow and a practical validation plan.

Your Starting Point	You Provide	We Build	You Receive
One bioactive compound has an unknown or incomplete target profile	SMILES, SDF, or defined structure Stereochemistry and compound identity Species, phenotype, and disease context Known targets or exclusions, if any	Compound standardization and conformers Broad or focused target-library definition Inverse docking and complementary prediction Structural and biological evidence integration	Ranked target hypotheses Confidence tiers and coverage statement Candidate sites and binding poses Prioritized validation experiments
A batch of related compounds needs a shared or differentiated target analysis	Structures for all compounds Comparable phenotypic or activity data Expected similarities and differences Preferred target universe	Matched compound preparation Cross-compound target profiling Shared-versus-selective target analysis SAR and evidence-convergence review	Compound-by-target hypothesis matrix Shared and differential target clusters Mechanism-relevant structural evidence Batch validation priorities
A phenotypic hit needs a mechanism-of-action hypothesis	Phenotype, cell type, species, and dose Time course and assay context Optional omics, resistance, or rescue data Known pathway observations	Phenotype-informed target-universe design Structural screening Pathway, expression, genetics, and ligand integration Mechanistic plausibility ranking	Mechanism-linked target shortlist Evidence table for each hypothesis Alternative explanations and limitations Biochemical, cellular, or genetic test plan
An efficacy signal or liability may arise from off-target binding	Compound and observed effect Exposure, concentration, and tissue context Primary pharmacology Safety or pathway concern	Liability-focused target panel Off-target structural screening Known-ligand and adverse-event evidence review Exposure-aware prioritization	Potential off-target ranking Effect-linked evidence summary Structural and confidence flags Counter-screen recommendations

Minimum starting point: one chemically defined compound plus species and biological context. A broad search is possible, but target coverage, structure quality, and assay tractability must be reported explicitly.

Illustrative result package

See What an Inverse Virtual Screening Project Delivers

The output is a ranked set of testable target hypotheses—not a claim that the top docking score is the true biological target.

Compound & Context Target Universe Cross-Target Screening Evidence Integration Validation Priorities

Explain Why a Target Was Prioritized

Each target is reviewed through several independent evidence layers.

Structural compatibility: pocket quality, pose plausibility, interactions, and cross-target score context.
Chemical evidence: known ligands, analog pharmacology, and chemotype relevance.
Biological context: expression, pathway, disease association, phenotype, genetics, and species.
Testability: available assays, controls, competing explanations, and confidence-limiting gaps.

Turn Ranking into Experimental Decisions

Targets are grouped by confidence and paired with the most discriminating next experiment.

High-priority targets with convergent evidence
Mechanistically plausible targets needing more context
Potential off-target or liability hypotheses
Assays designed to confirm or reject each candidate

Example Target-Hypothesis Ranking

Generic target labels are used for illustration. Real reports include target identifiers, structure provenance, evidence links, limitations, and validation recommendations.

Illustrative data

Rank	Target	Structural Fit	Known-Ligand Support	Biological Relevance	Confidence	Recommended Validation
01	Target A	Strong	Direct analog evidence	Phenotype and pathway aligned	High	Biochemical binding + cellular rescue
02	Target B	Strong	Same chemotype class	Disease-associated expression	High	Competition assay
03	Target C	Moderate	Related ligand support	Pathway consistent	Medium	Enzyme inhibition panel
04	Target D	Strong	Limited	Expression mismatch	Medium	Target engagement in relevant cells
05	Target E	Moderate	Known off-target class	May explain liability	Medium	Safety counter-screen
06	Target F	Uncertain	None located	Weak contextual support	Low	Hold pending orthogonal evidence

Interpretation note: cross-target docking scores are not automatically comparable, and structural compatibility alone cannot establish mechanism. Target hypotheses require orthogonal biochemical, biophysical, cellular, genetic, or competition-based validation.

Research applications

Where This Screening Strategy Creates Value

The method is selected for the scientific decision—not used as a one-size-fits-all calculation.

Mechanism-of-Action Discovery

Generate experimentally testable target hypotheses for cell-active or organism-level hits.

Off-Target Assessment

Investigate unexpected efficacy, adverse signals, or cross-reactivity across a defined protein panel.

Drug Repurposing

Prioritize new target–disease connections for approved, clinical, or shelved compounds.

Natural Product Deconvolution

Assess structurally complex bioactive molecules against relevant target families and pathways.

Polypharmacology Mapping

Identify a plausible multi-target profile when activity may arise from coordinated pathway modulation.

Metabolite and Environmental Chemical Research

Form target hypotheses for endogenous or exogenous small molecules with observed biological effects.

Decision-gated workflow

From Project Question to Experiment-Ready Shortlist

Frame the biological question

Define species, phenotype, disease context, known evidence, target classes, and the experimental budget for follow-up.
Build the target universe

Select relevant structures and target families, remove unsuitable entries, and document coverage and gaps.
Run cross-target screening

Generate comparable binding hypotheses using standardized preparation and scoring protocols.
Integrate orthogonal evidence

Combine structural results with known ligands, expression, pathways, genetics, disease association, and optional omics data.
Prioritize hypotheses

Rank targets by evidence convergence, novelty, biological plausibility, and experimental tractability.
Design confirmation

Recommend biochemical, biophysical, cellular, genetic, or competition assays that can confirm or reject each leading target.

Quality gates

Three Checks Before a Candidate Is Recommended

Gate 01

Input Fitness

Are the structure, ligand data, target panel, and library suitable for the chosen method?

Gate 02

Evidence Convergence

Do orthogonal scores, interactions, chemistry, and biological context support the same candidates?

Gate 03

Experimental Actionability

Can the shortlist be sourced, tested, interpreted, and used to make the next program decision?

Defined outputs

Deliverables Built for the Next Experimental Decision

Files, evidence, and recommendations are organized so your team can review the selection logic and move candidates into testing.

Target Ranking

Confidence tiers
Evidence summary
Coverage statement

Structural Hypotheses

Candidate sites
Docked poses
Key interaction maps

Biological Context

Pathway links
Disease relevance
Off-target flags

Validation Roadmap

Priority experiments
Controls
Go/no-go criteria

Related screening methods

Choose the Evidence Route That Matches Your Project

Methods can be used alone or combined as a consensus workflow when the inputs and decision justify it.

FAQs

Frequently Asked Questions

It is a hypothesis-generation component of target identification. A credible result combines computational ranking with biological context and orthogonal experimental validation.

The library can be broad, disease-focused, organism-specific, or limited to a protein family. Selection reflects structural coverage, biological relevance, and the practical follow-up assays available.

Yes, provided a chemically meaningful structure and relevant stereochemistry can be defined. Flexible or unusual chemistry may require additional conformational and parameter checks.

You receive ranked target hypotheses, structural and biological evidence for each leading candidate, limitations, and a prioritized validation plan.