Billion-scale screening & AI-driven hit discovery

Ultra-Large Library Virtual Screening Service

CD ComputaBio delivers high-throughput virtual screening against ultra-large chemical libraries (10⁹–10¹² compounds), combining AI-accelerated docking, deep learning scoring, and hierarchical filtering to identify novel hits with unprecedented efficiency. We bridge computational triage with targeted wet-lab validation to de-risk your early discovery pipeline.

Launch a Screening Campaign Consult a Screening Strategist View Workflow

10⁹+ compound libraries AI-accelerated docking Deep learning rescoring Integrated wet-lab validation

From ultra-large libraries to viable hitsWe screen billions of make-on-demand or virtual compounds, applying physics-based and AI filters to surface only the most promising scaffolds.

Hierarchical triage at scaleFast shape-based similarity, pharmacophore matching, and ML docking enable rapid pruning before high-accuracy rescoring.

Wet-lab feedback loopWe prioritize candidates for biochemical assays and provide iterative refinement based on experimental binding data.

When to use ultra-large library screening?

Use this service when you need to explore vast chemical space, find novel chemotypes for challenging targets, or accelerate hit discovery beyond traditional HTS limits.

You need to screen billions of compounds for a novel target.

You want to find hits beyond known active chemotypes.

You have a well-defined binding site and a validated structure.

You need to prioritize compounds for experimental testing efficiently.

You aim to reduce wet-lab screening cost and turnaround time.

Discuss Your Screening Strategy

Ultra-Large Library Screening Coverage

Library design

Chemical space exploration

Access ultra-large virtual libraries (Enamine REAL, ZINC22, Mcule, etc.) and custom make-on-demand catalogs to maximize novel scaffold coverage.

Billion-scale compound selection
Diversity and novelty filtering

AI docking

Deep learning-accelerated docking

Use GPU-accelerated docking (AutoDock-GPU, Vina, Gnina) with convolutional neural networks for rapid pose prediction and scoring.

Molecular docking service
CNN-based scoring functions
Ensemble docking

Hit triage

Hierarchical filtering cascade

Multi-stage funnel combining pharmacophore, shape, and interaction fingerprint filters to reduce billions to hundreds of candidates.

Pharmacophore modeling
Shape similarity screening

Rescoring

MM-GBSA & interaction fingerprint rescoring

Refine top hits with MM-GBSA and interaction fingerprint analysis to prioritize true binders and reduce false positives.

Binding free energy analysis
MM-GBSA rescoring

Wet-lab integration

Experimental validation support

We coordinate with biochemical assay partners to test prioritized compounds, enabling rapid hit confirmation and SAR feedback.

Biochemical assay design
Hit confirmation & IC₅₀ determination

Novel targets

Challenging target screening

Apply ultra-large screening to difficult targets such as protein-protein interfaces, allosteric sites, and cryptic pockets.

Cryptic pocket detection
Allosteric modulator discovery

Design Your Screening Cascade

Core Technologies for Ultra-Large Screening

AI/ML

Deep Learning Scoring & Ranking

Leverage trained neural networks (e.g., Gnina, DeepDock) to predict binding poses and affinities with superior accuracy and speed.

3D convolutional neural networks
Transfer learning for target-specific models
Active learning for iterative screening

Docking

GPU-Accelerated Molecular Docking

Screen billions of compounds in days using optimized docking engines on cloud GPU clusters.

AutoDock-GPU, Vina, and Gnina
Ensemble docking for receptor flexibility
Consensus scoring

MD & Rescoring

Physics-Based Rescoring

Apply MM-GBSA and molecular dynamics to top-ranked hits for accurate binding affinity prediction and stability assessment.

Molecular dynamics simulation
MM-GBSA / MM-PBSA
Interaction fingerprint analysis

Select Your Screening Technology

Ultra-Large Library Screening Workflow

Target preparation & binding site analysis

Curate protein structure, identify druggable pockets, and define pharmacophore constraints for screening.

Ultra-large library assembly

Aggregate 10⁹–10¹² compounds from virtual catalogs and custom make-on-demand libraries.

AI-accelerated docking & primary filtering

Perform GPU-accelerated docking with ML scoring to rank billions of compounds and retain top 0.1–1%.

Hierarchical rescoring & clustering

Apply MM-GBSA, interaction fingerprints, and scaffold clustering to eliminate false positives and enhance diversity.

Final ranking & candidate selection

Integrate MM-GBSA and interaction fingerprint analysis to rank top candidates and select 50–200 compounds for experimental testing.

Wet-lab validation & iterative optimization

Coordinate biochemical assays, analyze results, and refine screening models for subsequent rounds.

Start Your Ultra-Large Screening

Which Ultra-Large Screening Strategy Fits Your Target?

Research Objective	Recommended Strategy	Key Readouts	Decision Supported
Discover novel hits for a well-defined pocket	AI docking + hierarchical filtering	Top-ranked scaffolds, docking scores	Select compounds for synthesis
Find allosteric or cryptic pocket binders	Ensemble docking + MD-based pocket detection	Pocket volume, ligand residence	Prioritize allosteric modulators
Optimize hit-to-lead with ultra-large libraries	Similarity search + MM-GBSA rescoring	Binding scores, SAR trends	Guide medicinal chemistry
Screen for resistance-breaking inhibitors	Mutant structure docking + MD analysis	Binding mode changes, selectivity	Design broad-spectrum compounds

Match Strategy to Your Target

Inputs Required

Protein structure (PDB, cryo-EM, or homology model) with defined binding site.
Ligand reference (co-crystal ligand or known active) for pharmacophore modeling (optional).
Target product profile: desired potency, selectivity, and ADMET constraints.
Library preference (e.g., Enamine REAL, ZINC22, custom).
Wet-lab validation capacity and timeline.

Deliverables

Ranked list of top 50–200 virtual hits with docking scores and binding modes.
Scaffold clustering and novelty analysis.
MM-GBSA rescoring and interaction fingerprint results for top candidates.
Experimental validation plan and assay recommendations.
Comprehensive report with figures, tables, and actionable insights.

Evaluate Your Screening Readiness

Real Research Scenarios We Solve

Oncology

Kinase inhibitor discovery

Screen billions of compounds against mutant kinases (e.g., EGFR, KRAS) to identify novel inhibitors with high selectivity and potency.

Infectious disease

Antiviral & antibiotic leads

Discover broad-spectrum antivirals targeting viral proteases and polymerases, or antibiotics against resistant bacterial enzymes.

GPCRs

GPCR ligand discovery

Apply ultra-large screening to membrane protein targets, including class A and B GPCRs, to find novel agonists or antagonists.

Request a Target-Specific Plan

Case Study

Case 1

Novel CDK2 Inhibitors from 1.2 Billion Compounds

Using AI-accelerated docking against a 1.2 billion compound library, we identified 12 novel CDK2 inhibitors with IC₅₀ values below 100 nM, including a new chemotype with high selectivity over CDK1.

Case 2

Allosteric Binders for a Challenging Phosphatase

We combined cryptic pocket detection with ultra-large screening to find first-in-class allosteric inhibitors for a phosphatase target, validated by SPR and cellular assays.

Discuss a Similar Screening Case

FAQ

How large is an ultra-large library?

We routinely screen libraries ranging from 10⁹ to over 10¹² compounds, including make-on-demand catalogs from Enamine REAL, ZINC22, and custom virtual libraries.

What is the typical turnaround time?

A standard ultra-large screening campaign (10⁹ compounds) takes 2–4 weeks for computational triage, with an additional 2–3 weeks for rescoring and candidate selection.

Do you offer wet-lab validation?

Yes, we partner with biochemical assay providers to test top candidates, including fluorescence polarization, SPR, and cell-based assays. We also support hit confirmation and IC₅₀ determination.

Can you screen for covalent inhibitors?

Absolutely. We have workflows for covalent docking and warhead screening against cysteine, lysine, and serine residues, integrated with our ultra-large libraries.

Ask an Ultra-Large Screening Expert