Research-informed screening strategy

Ligand-Based Virtual Screening Services: Expand from Known Active Chemistry

When reliable active compounds exist but the target structure is unavailable or uncertain, ligand-based screening can search by molecular similarity, three-dimensional shape, physicochemical profile, pharmacophore features, or predictive models. The project is calibrated to the available data and the experimental decision you need to make.

Discuss your project Choose a screening route

Ligand-based virtual screening using known active molecules and chemical similarity

Problems this service solves

Find New Chemistry Without Relying on a Target Structure

Use experimentally supported ligand evidence to expand analogs, identify alternative scaffolds, and prioritize compounds your team can test.

A known active needs analogs

Search for close or property-matched neighbors to support SAR expansion and confirmatory testing.

A program needs scaffold diversity

Use fingerprints, shape, and feature-based similarity to identify alternative chemotypes.

The target structure is missing or low confidence

Build a screening strategy from activity data without depending on a docking-ready structure.

Start from the ligand evidence you already have

Can We Support Your Ligand-Based Screening Project?

Match your available compound evidence to the data we need, the ligand-based workflow we can build, and the experiment-ready outputs you will receive.

Your Starting Point	You Provide	We Build	You Receive
One or a few confirmed active compounds	SMILES, SDF, or compound IDs Activity values and assay context Target profile, if known Desired library and test capacity	Structure and activity curation 2D similarity and analog searching 3D shape or feature matching Property and diversity filtering	Ranked analog candidates Similarity and neighbor evidence Scaffold clusters and property flags Purchasable test shortlist
An active and inactive compound dataset	Standardized structures or raw files Activity type, units, and assay conditions Series labels or train/test split, if available Selectivity, property, and IP constraints	Dataset harmonization and quality review Descriptor and representation selection QSAR or classification model development Validation and applicability analysis	Curated modeling dataset Performance and validation metrics Ranked predictions with uncertainty flags Diverse candidates for experimental testing
A phenotypic hit or active series without a reliable target structure	Hit structures and phenotype information Cell, species, pathway, or disease context Known SAR or inactive analogs Desired novelty and property profile	Ligand-evidence assessment Shape, pharmacophore, or feature modeling Scaffold-hopping search Novelty and developability triage	Alternative chemotype families Feature or shape alignment evidence Applicability and uncertainty notes Primary and backup test compounds
A proprietary or commercial library to prioritize	SDF or SMILES file with traceable IDs Source and availability information Reference actives or trained model Assay capacity and final-set size	Library standardization Similarity, shape, or model scoring Clustering and redundancy control Property, alert, and availability review	Full ranked result table Score and query attribution Clustered candidate families Balanced experimental selection

Minimum starting point: one credible active compound plus a clear search objective can support an initial feasibility review. Additional inactive compounds, comparable activity data, and assay context expand the methods that can be validated.

Illustrative ligand-based screening results

See What a Virtual Screening Project Delivers

A ligand-based project connects similarity and feature evidence to chemical diversity, property review, risk flags, and a practical decision about which compounds should move into testing.

01 Reference Ligands

02 Search Library

03 2D & 3D Scoring

04 Diversity & Risk Review

05 Test Shortlist

Illustrative ligand-based virtual screening results with a 2D fingerprint and 3D shape similarity landscape and prioritized candidate table — Illustrative result dashboard: similarity scores are reviewed together with scaffold diversity, molecular properties, risk flags, and experimental action.

How to read the result

Evidence Behind Each Recommendation

2D fingerprint similarity identifies related chemical neighborhoods.
3D shape similarity captures spatial resemblance beyond a single substructure.
Scaffold assignment prevents the final set from collapsing into one chemotype.
Property review includes molecular weight, lipophilicity, alerts, and project-specific limits.
Decision status separates candidates ready to test from those requiring review or removal.

Test Strong combined evidence and suitable for the proposed experimental set.

Review Potentially useful, but redundancy, properties, or uncertainty require manual assessment.

Hold Retained for traceability but not recommended for the immediate test set.

Example scaffold-diverse ligand-based virtual screening candidate structures labeled VS-001 through VS-013

Scaffold-Diverse Candidate Set

Selected structures can be presented with anonymized or project-specific IDs, cluster membership, source-library traceability, and the reason each compound was retained.

Optional structural review of a ligand-based screening candidate in a protein binding pocket

Optional Structural Cross-Check

When a credible target structure and binding site are available, shortlisted ligands can undergo docking or pose review as an additional evidence layer. This is optional and is not required for a ligand-based screen.

Scientific interpretation: similarity and model scores prioritize hypotheses; they do not establish binding, potency, selectivity, or biological activity. Illustrative values and candidate IDs are synthetic. Final conclusions require experimental testing.

Research applications

Where Ligand-Based Screening Creates Value

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

Analog Expansion

Build a focused set around confirmed hits to accelerate structure–activity relationship studies.

Scaffold Hopping

Retain key shape or interaction features while moving into structurally distinct chemical series.

Target-Structure-Free Discovery

Prioritize candidates when structural biology is incomplete but ligand evidence is available.

Activity Prediction

Use curated quantitative data to build models that rank untested compounds within a defined applicability domain.

Phenotypic Hit Expansion

Search around cell-active compounds even when the molecular target has not yet been established.

Library Enrichment

Create a smaller, activity-enriched subset for experimental high-throughput screening.

Decision-gated workflow

From Known Ligands to a Diverse Test Set

Audit the evidence

Review compound identity, assay comparability, activity units, duplicates, and the balance of active and inactive examples.
Choose the representation

Select fingerprints, shape, electrostatics, pharmacophore features, or descriptors that match the data and search goal.
Build and validate

Calibrate similarity thresholds or train models with appropriate splits, controls, and applicability checks.
Search chemical space

Score the requested compound collection and retain traceable links to query molecules and model features.
Select a diverse panel

Balance predicted activity, structural novelty, physical properties, alerts, and availability.
Plan the learning cycle

Deliver the shortlist with an assay and data-return plan that can improve the next screening round.

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

What Your Ligand-Based Screening Project Delivers

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

Curated Input Set

Standardized structures
Activity table
Curation log

Model Evidence

Validation results
Applicability domain
Feature or neighbor context

Candidate Set

Ranked hits
Scaffold clusters
Property and alert annotations

Experiment Plan

Suggested test set
Controls
Iteration strategy

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

Similarity and shape searches may begin from one or a few credible actives. Predictive models usually require a larger, chemically diverse, and consistently measured dataset. We select the method after reviewing data quantity and quality.

Yes. Shape, pharmacophore, and feature-based approaches can prioritize compounds that differ in 2D structure while preserving a relevant molecular pattern.

Yes. Reliable inactives help define decision boundaries, evaluate enrichment, reduce obvious false positives, and improve supervised models.

We first assess whether the data can be harmonized. When conditions are not comparable, we may separate datasets, use classification rather than regression, or recommend a narrower evidence set.