Ref B5
Computational Service
Confidence Reported
Scoped After Feasibility Review
Service 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.
Key Features & 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
1Finding novel chemical matter when catalogue screening has been exhausted
2Escaping a crowded IP landscape by exploring scaffolds outside published series
3Hit finding against difficult targets where large sampling is the main lever available
4Expanding around a confirmed hit by searching on-demand space for accessible analogues
5Building a compound acquisition list that maximises diversity per unit of budget
Methods and Techniques Applied
1Definition of the searchable chemical space, including reagent and reaction filters for synthesisability
2Conventional docking of a representative training subset to generate labelled data
3Training of learned scoring or surrogate models, with held-out validation and enrichment assessment
4Iterative active-learning rounds concentrating sampling on promising regions of the space
5Rigorous re-docking of the top fraction, with structure-based rescoring and pose inspection
6Diversity clustering, novelty checking and final selection against your purchasing constraints
What We Need From You
1Target structure and binding site, ideally with a validated docking protocol or a reference ligand
2Known actives, inactives and any chemotypes to avoid for IP reasons
3Budget and timeline for compound synthesis, since on-demand compounds carry lead times
4Preferred chemical space or supplier, if your organisation has existing arrangements
What You Receive
1Ranked shortlist with supplier identifiers, estimated synthesis success rate and lead time
2Re-docked poses and interaction analysis for every shortlisted compound
3Model performance report: training data, validation metrics, enrichment and known failure modes
4Diversity and novelty analysis of the selected set relative to known actives and published chemotypes
5Full protocol documentation enabling the screen to be repeated or extended later
What We Compute and Report
1Learned-model score across the full enumerated space, with model validation statistics
2Conventional docking score and pose for every compound reaching the shortlist
3Estimated synthesis success probability and lead time per selected compound
4Novelty distance from known actives and from published chemotypes
5Diversity statistics quantifying how much of the searched space the selection represents
Questions We Are Asked Before Scoping
Are on-demand compounds actually deliverable?
▸Most are, but not all. Suppliers report synthesis success rates in the region of seventy to ninety percent with lead times of several weeks. We include this in the shortlist so you can over-select accordingly.
Is a learned score as good as docking?
▸It is an approximation of docking, not an improvement on it, and it inherits docking’s limitations. That is exactly why the shortlist is re-docked conventionally before delivery.
Does screening more compounds guarantee better hits?
▸It improves the chance of finding potent and novel chemotypes, but scoring accuracy remains the binding constraint. Scale widens the search; it does not fix the scoring function.
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.
Is This the Right Service for You?
Choose this 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
These are the failure modes that produce confident-looking results which do not survive the next experiment. Each one is designed against rather than discovered afterwards.
Learned scores inheriting the errors of the docking they were trained on
Machine-learned scores are used for triage only; every shortlisted compound is re-docked conventionally and inspected.
On-demand compounds that never get synthesised
Expected synthesis success rate and lead time are reported per compound so selections can be over-ordered accordingly.
Novelty claimed on the basis of structure alone
Similarity to known actives and to published series is computed and reported; patentability remains a legal judgement.
A Worked Example
Searching make-on-demand space for a new chemotype
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.
What Published Work Reports
The results below were published by independent groups and are cited to show what these computational approaches have achieved in the literature. They are not ICDMO project results, and outcomes vary substantially between targets, datasets and programmes. We do not promise comparable results for any particular project.
A 22% confirmed hit rate from docking 138 million compounds
In a landmark 2019 study, ~138 million make-on-demand compounds were docked against the dopamine D4 receptor. Of 549 compounds selected and tested, 122 were confirmed active — a 22% hit rate — including ligands in the sub-nanomolar range. The same work screened ~99 million compounds against AmpC β-lactamase, where 5 of 44 tested compounds were confirmed (11%), one of which was among the most potent non-covalent AmpC inhibitors reported at the time.
Lyu et al., Nature, 2019 ↗Hit rate rises with rank, which is why library scale matters
The same body of work showed that the probability of a compound being confirmed active increases as its docking rank improves, and that larger libraries push better-scoring molecules into the selected set. This is the practical argument for screening make-on-demand space rather than a physical catalogue — and also the reason scoring accuracy, not library size alone, remains the limiting factor.
Lyu et al., Nature, 2019 ↗Service Workflow
1
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.
2
Feasibility Review
We check that the data, target class and available structural evidence can support the proposed approach, and say plainly where they cannot.
3
Technical Proposal
Scope, methods, deliverables, timeline and price are confirmed in writing, together with the limitations we expect to report.
4
Computation and QC
The analysis runs on pinned tool versions in a containerised environment, with quality checkpoints and method comparison where that is informative.
5
Report and Follow-Up
You receive the ranked results, the documented methods, the limitations and a proposed validation plan, plus support in interpreting them.
Service Process
1
Online Consultation
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2
Solution Matching
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3
Service Contract
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4
Project Execution
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5
Data Delivery
Specifications & Turnaround
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 a Custom Quote
Our scientific team responds within 24 hours with a detailed technical proposal and pricing tailored to your project.
Contact Us Online ConsultationStandard Deliverables
✓Sequence-verified final product
✓Full QC documentation
✓Certificate of Analysis (CoA)
✓Electronic data files
✓Project completion report
Why ICDMO?
Computational CDMO Focus
Reproducible, Versioned Pipelines
Confidence & Limitations Reported
Experiment-Aware Recommendations
Response Within One Business Day