AI-Powered Ultra-Large Library Screening

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Overview

Make-on-demand chemical spaces now run into the billions of compounds, far beyond what conventional docking can process. We screen these spaces using machine-learned scoring and active-learning strategies: a small fraction of the library is docked conventionally, a model is trained on those results, the model scores the remainder, and the most promising fraction is returned for rigorous re-docking. The result is access to chemical diversity that a catalogue screen cannot reach, at a compute cost that remains defensible.

Schematic: screening runs as a cascade. Cheap filters remove the bulk, expensive scoring is applied only to survivors, and the attrition at every stage is reported.

Key Advantages

Billion-Scale Coverage

Active learning and learned scoring make make-on-demand spaces such as Enamine REAL tractable, opening up scaffolds absent from any physical collection.

Confirmed By Physics

Machine-learned scores are used for triage only. Everything reaching the shortlist is re-docked conventionally and inspected, so no compound is recommended on a neural network score alone.

Synthesisability Built In

Screening is confined to synthetically accessible on-demand space with stated success rates and lead times, so hit lists translate into deliverable compounds rather than paper molecules.

Novelty and IP Awareness

Shortlists are checked against known actives and patent-adjacent chemotypes, so novelty can be part of the selection criteria instead of an afterthought.

Where This Service Is Used

Finding novel chemical matter when catalogue screening has been exhausted

Escaping a crowded IP landscape by exploring scaffolds outside published series

Hit finding against difficult targets where large sampling is the main lever available

Expanding around a confirmed hit by searching on-demand space for accessible analogues

Building a compound acquisition list that maximises diversity per unit of budget

Methods and Techniques Applied

Definition of the searchable chemical space, including reagent and reaction filters for synthesisability

Conventional docking of a representative training subset to generate labelled data

Training of learned scoring or surrogate models, with held-out validation and enrichment assessment

Iterative active-learning rounds concentrating sampling on promising regions of the space

Rigorous re-docking of the top fraction, with structure-based rescoring and pose inspection

Diversity clustering, novelty checking and final selection against your purchasing constraints

What We Need From You

Target structure and binding site, ideally with a validated docking protocol or a reference ligand

Known actives, inactives and any chemotypes to avoid for IP reasons

Budget and timeline for compound synthesis, since on-demand compounds carry lead times

Preferred chemical space or supplier, if your organisation has existing arrangements

What You Receive

Ranked shortlist with supplier identifiers, estimated synthesis success rate and lead time

Re-docked poses and interaction analysis for every shortlisted compound

Model performance report: training data, validation metrics, enrichment and known failure modes

Diversity and novelty analysis of the selected set relative to known actives and published chemotypes

Full protocol documentation enabling the screen to be repeated or extended later

What We Compute and Report

Learned-model score across the full enumerated space, with model validation statistics

Conventional docking score and pose for every compound reaching the shortlist

Estimated synthesis success probability and lead time per selected compound

Novelty distance from known actives and from published chemotypes

Diversity statistics quantifying how much of the searched space the selection represents

Interpretation and Limitations

Computational predictions prioritise hypotheses; they do not independently establish binding, biological activity, therapeutic efficacy, safety or clinical validity. Performance depends on input quality, target class, the structural evidence available and the applicability domain of the selected methods. Scores used for ranking are comparative devices and should not be read as measured quantities. All deliverables are for research use only and are not intended for diagnostic or therapeutic use.

Specifications

Item	Specification	Notes
Library scale	10⁸–10¹⁰ compounds	Make-on-demand and combinatorial spaces
Typical turnaround	4–8 weeks	Including active-learning rounds and confirmatory docking
Shortlist size	50–500 compounds	Sized to your synthesis and assay budget
Confirmation	100% re-docked	No compound is delivered on learned score alone

Service Workflow

Step 01

Scientific Consultation

We discuss the objective, the decision it supports and the data you hold. Nothing is quoted before we understand what the result has to answer.

Step 02

Feasibility Review

We check that the data, target class and available structural evidence can support the proposed approach, and say plainly where they cannot.

Step 03

Technical Proposal

Scope, methods, deliverables, timeline and price are confirmed in writing, together with the limitations we expect to report.

Step 04

Computation and QC

The analysis runs on pinned tool versions in a containerised environment, with quality checkpoints and method comparison where that is informative.

Step 05

Report and Follow-Up

You receive the ranked results, the documented methods, the limitations and a proposed validation plan, plus support in interpreting them.

Is This the Right Service for You?

Choose this service when

Catalogue screening has been exhausted and genuinely novel chemotypes are needed

The IP landscape around existing series is crowded and different scaffolds are required

Budget allows compound synthesis with multi-week lead times rather than off-the-shelf purchase

Consider another service when

Compounds are needed within days and novelty is secondary

High-Throughput Virtual Screening

You want molecules designed into the pocket rather than selected from a space

Generative Molecular Design

What Goes Wrong, and How We Handle It

The failure modes that produce confident-looking results which do not survive the next experiment. Each one is designed against rather than discovered afterwards.

Machine-learned scores are used for triage only; every shortlisted compound is re-docked conventionally and inspected.

Case Studies

The situation

Three rounds of catalogue screening had produced only analogues of a known, patented series.

What the work involved

A representative training subset was docked conventionally, a surrogate model was trained and validated on held-out data, several active-learning rounds concentrated sampling, and the top fraction was re-docked rigorously.

What came back

Three hundred compounds spanning nine scaffold families, all at least a defined similarity distance from the patented series, with synthesis feasibility reported per compound.

Illustrative of the shape this work takes. Details are generalised, no client or target is identified, and scope and outcome vary with the system in question.

Questions We Are Asked Before Scoping

Note: ICDMO delivers computational (in silico) services. Results prioritise hypotheses for testing; they do not by themselves establish binding, biological activity, efficacy or safety. All deliverables are for research use only and are not intended for diagnostic or therapeutic use. Downstream experimental work is scoped separately and executed through qualified partners.

Get in Touch
about AI-Powered Ultra-Large Library Screening

Tell us about the target, the data you hold and the decision the result needs to support. A scientist reviews every enquiry and replies within one business day.

Feasibility reviewed before any scope or price is fixed

Methods, parameters, confidence and limitations in every report

Confidentiality agreement available before data is shared