Ref N1
Computational Service
Confidence Reported
Scoped After Feasibility Review
Service Overview
A focused library is a bet about where actives are likely to be found, and the quality of that bet determines the hit rate. We design target-class libraries — kinase, GPCR, protease, epigenetic, ion channel — by combining privileged scaffold knowledge, pharmacophore requirements from known actives, structure-based filtering against your target and property-based developability filters. Selections are clustered for diversity and sourced against real catalogues so the library can be bought rather than admired.
Key Features & Advantages
Target-Class Knowledge Applied
Scaffold and pharmacophore preferences established for the target class shape the selection, which is what separates a focused library from a diverse one.
Structure-Based Filtering Where Possible
When a structure exists, docking and shape filtering are used to bias the selection toward compounds complementary to your specific pocket, not merely to the class.
Diversity Measured And Enforced
Selections are clustered so that chemotype coverage is maximised for the number of compounds purchased, avoiding libraries dominated by a handful of series.
Purchasable By Construction
Compounds are sourced against current catalogues with availability, price band and purity information, so the design converts directly into a purchase order.
Where This Service Is Used
1Assembling a screening library for a newly started target programme
2Building a target-class library to support several programmes at once
3Expanding an internal collection where chemotype coverage is weak
4Designing a follow-up set around confirmed hits for SAR exploration
5Selecting a fragment library shaped to a specific target family
Methods and Techniques Applied
1Definition of the target class profile and the pharmacophore requirements from known actives
2Catalogue assembly and standardisation with removal of problematic chemistry and reactive groups
3Property, drug-likeness and ADMET pre-filtering to the agreed developability window
4Similarity, shape and pharmacophore screening against known actives for the class
5Structure-based docking filters where a target structure is available
6Diversity clustering, coverage analysis and final selection with supplier sourcing
What We Need From You
1The target or target class and, where available, the structure
2Known actives for the target or class, which strongly shape the selection
3Library size, budget and any preferred suppliers or existing purchasing arrangements
4Assay format and constraints, including solubility and interference requirements
What You Receive
1Final compound list with structures, supplier identifiers, catalogue numbers and price bands
2Property and diversity analysis of the selected set with coverage statistics
3Docking results and predicted interactions where structure-based selection was used
4Selection rationale documenting every filter and threshold applied
5Plate layout suggestions and any solubility or handling flags
What We Compute and Report
1Property and drug-likeness distributions across the selected set
2Diversity and chemotype coverage statistics quantifying what the library samples
3Docking scores and predicted interactions where structure-based selection was applied
4Per-compound supplier availability, catalogue identity and price band
5Attrition at each filter stage, so the selection is fully traceable
Questions We Are Asked Before Scoping
What hit rate can we expect?
▸Focused libraries typically outperform diverse ones for a known target class, but rates vary widely with target and assay. We report the design rationale and enrichment where known actives allow it to be estimated.
Diverse or focused library?
▸Focused when the target class is well characterised and actives are known; diverse when the target is novel or the class is poorly precedented. We advise based on your case and can design either.
Can you check availability before we order?
▸Yes. Selections are sourced against current catalogues with availability and price at the time of delivery, though stock changes and some items will need substituting.
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
A screening collection must be assembled for a new programme or target class
An internal collection needs standardising, filtering and gap analysis
A follow-up set is needed around confirmed hits for SAR exploration
Consider another service when
You want to screen computationally rather than buy compounds
High-Throughput Virtual Screening Novel chemistry must be designed rather than selected
De Novo Small Molecule 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.
A large library that samples very little chemical space
Diversity and coverage statistics are computed, so size is not mistaken for breadth.
Interference chemotypes bought in bulk
PAINS, aggregators and reactive groups are removed during design rather than diagnosed after a screening campaign.
Selections that cannot actually be purchased
Availability and price band are checked at delivery, with the caveat that catalogue stock changes.
A Worked Example
Building a kinase-focused set on a fixed budget
The situation
The team could buy roughly 5,000 compounds and wanted the best coverage for a kinase programme.
What the work involved
Catalogues were standardised and filtered, target-class pharmacophores were applied, structure-based filtering was used against the specific pocket, and the selection was clustered for diversity before sourcing.
What came back
A 5,000-compound list spanning the privileged hinge-binding chemotypes with coverage statistics reported, roughly 900 compounds removed as interference-prone before purchase.
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.
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