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ICDMO AI Services

AI Digital Library Construction Services

AI-Based High-Throughput Gene & Protein Interaction Screening Libraries

Build digital 3D protein libraries covering whole genomes, transcription factors or custom gene sets. No reagents required for the computational phase, and every build ships with its source databases, coverage and per-entry model confidence.

10⁶–10⁹

Library scale

2–5 wk

Typical turnaround

SDF/SMILES

Delivery formats

Versioned

Provenance recorded

Illustrative image representing compound and biological libraries

No Reagents for the In Silico Phase

Confidence-Scored Results

Publication-Ready Figures

Validation Planned With Partners

Response Within One Business Day

Service Overview

ICDMO's AI-powered digital library construction service simulates vast species' gene pools and builds comprehensive 3D protein structural libraries optimized for high-throughput interaction screening. Rather than constructing physical yeast or phage display libraries (which require months), our platform generates AI-predicted 3D protein models for every gene in your target species, creating a complete digital library ready for immediate large-scale screening. Libraries are validated against known interaction data and continuously updated with the latest genomic databases.

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.

Technical Advantages

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No Physical Library to Build

The computational phase requires no reagents, cloning or transformation — removing the cost and months of lead time that physical library construction carries.

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Weeks, Not Months

Far faster than constructing a physical library. The delivery date is fixed during scoping, since it depends on library size, species and the structural evidence available.

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Model Organisms and Custom Species

Whole-genome libraries for major model organisms and crops, plus custom species built on request. Species coverage and build date are documented with each delivery.

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3D Structure + Core Interaction Analysis

Each library entry includes AI-generated 3D protein model, modeling confidence score, and core interaction site analysis.

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Seamlessly Integrated with Screening

Directly compatible with Library-to-Library Interaction Screening, AI-PPI, and Binding Site Prediction services.

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Optional Yeast Validation

One-on-one yeast validation services available to experimentally confirm key digital hits.

Available Digital Library Types

Transcription Factor (TF) Digital Libraries — 21 Plant Species

▸Soybean (Glycine max): 6,150 TFs

▸Wheat (Triticum aestivum): 3,606 TFs

▸Maize (Zea mays): 3,308 TFs

▸Arabidopsis (A. thaliana): 2,296 TFs

▸Rice, Cotton, Tobacco, Tomato, and 13 additional species

▸Custom TF libraries for any species — provide genome annotation

Whole Genome Digital Libraries — 50+ Species

▸Arabidopsis thaliana: 27,434 genes

▸Soybean (Glycine max): 55,799 genes

▸Rice (Oryza sativa): 43,649 genes

▸Homo sapiens: 23,391 genes

▸Mus musculus: 21,615 genes

▸Danio rerio (Zebrafish): 24,664 genes

▸E. coli: 4,363 genes | S. cerevisiae: 6,039 genes

▸Pathogens, parasites, and custom organisms available

Species-Specific & Custom Libraries

▸Banana (Musa acuminata) auxin/gibberellin-related: 3,528 genes

▸Pathway-specific libraries: hormone signaling, immune response, metabolic networks

▸Custom gene set libraries: provide amino acid or CDS sequences

▸Cross-species interaction libraries for virus–host or symbiosis studies

How Digital Libraries Are Used

1Library-to-Library Screening: Screen bait library A × prey library B for all pairwise interactions (millions of pairs)

2One-to-Many Screening: Screen a single protein of interest against an entire genome-wide library

3TF-Target Identification: Identify transcription factor binding partners across the proteome

4Virus–Host Interaction: Cross-species screening to map pathogen–host interaction networks

5Drug Target Discovery: Screen target protein against compound or peptide digital libraries

What We Need From You

1The intended use: screening a specific target, building a corporate collection, or supporting several programmes

2Target or target-class information, which allows the library to be focused rather than merely large

3Property, chemistry and novelty constraints, including any substructures to exclude

4Supplier arrangements, budget and whether compounds must be physically purchasable

5Any existing internal collection, so that the new library complements rather than duplicates it

How the Analysis Is Performed

1Source assembly from commercial catalogues, make-on-demand spaces and any client collection

2Structure standardisation: salt stripping, tautomer canonicalisation, stereochemistry handling and deduplication

3Filtering for drug-likeness, reactive groups, PAINS, aggregators and predicted ADMET liabilities

4Focusing by pharmacophore, scaffold or target-class preference where the library is purpose-built

5Conformer generation and preparation so the library is screening-ready rather than a plain structure list

6Diversity clustering and coverage analysis against the intended chemical space

What You Receive

1Prepared library in SDF and SMILES with standardised structures, protonation states and generated conformers

2Property and developability annotation for every compound in the set

3Diversity and coverage analysis showing which regions of chemical space are represented

4Provenance record: source catalogues, versions, filters applied and the attrition at each stage

5Supplier and availability information where the library is intended for physical purchase

Interpretation and Limitations

A digital library is an enumeration, not an inventory. Make-on-demand compounds carry synthesis success rates below one hundred percent and lead times of several weeks, and catalogue availability changes between design and purchase. Property annotations are predictions and inherit the applicability-domain limits of the underlying models. We report all of this with the library rather than after the order is placed.

Questions We Are Asked Before Scoping

Focused or diverse library?

▸Focused when the target class is well precedented and actives are known; diverse when the target is novel. We advise based on your programme and can build either, or a tiered set combining both.

Are the compounds purchasable?

▸Where that is a requirement, yes — selections are sourced against current catalogues with availability and price band recorded. Make-on-demand compounds are labelled as such with their expected lead time.

Can you prepare our internal collection?

▸Yes. Standardising, filtering and annotating an existing corporate collection is a common request and frequently reveals duplicates, degraded structures and liability chemotypes that were never flagged.

Technical Workflow

Scientific Consultation

We discuss the objective, the decision the result has to support and the data you already hold. Nothing is quoted before that is clear.

Feasibility Review

We check that your inputs, target class and available evidence can support the approach, and say plainly where they cannot.

Technical Proposal

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

Computation

Sources are assembled and standardised, filters and focusing criteria are applied in agreed order, and the surviving set is prepared, clustered and annotated.

Report and Follow-Up

You receive the screening-ready library with property annotation, diversity analysis and a complete provenance record.

Service Timeline & Deliverables

Service Content	Turnaround	Deliverables
Sequence extraction / sorting from provided input	1 working day	Complete Excel data sheet with all gene/protein IDs and annotations
Complete 3D structural modeling of all library members	14 working days	3D Modeling Scoring Table with model confidence scores; PDB files for all proteins; Core interaction site analysis
Custom species library (genome annotation required)	14–21 working days	Custom digital library ready for immediate screening
Optional: downstream Library-to-Library Screening	Per screening timeline	Interaction network Excel, ranked pairs, visualization

Is This the Right Service for You?

Choose this when

A screening-ready compound collection must be assembled and prepared

An existing internal collection needs standardising and annotating

Library composition must be justified before purchase

Consider another service when

The library is antibody or peptide rather than small molecule

Antibody Library Design

You want compounds screened rather than assembled

AI Multimodal Protein–Compound Screening

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.

An enumerated space presented as an available inventory

Make-on-demand entries are labelled with expected synthesis success and lead time, separately from catalogue stock.

Duplicates, salts and degraded structures inherited from source files

Standardisation, deduplication and structure checking run before any filtering or annotation.

Property annotations applied outside their applicability domain

Compounds outside model domains are flagged rather than given a confident-looking number.

A Worked Example

Cleaning a corporate collection before a screening campaign

The situation

An inherited collection of 180,000 structures had never been standardised or filtered.

What the work involved

Structures were standardised and deduplicated, reactive and interference chemotypes were identified, properties were annotated with domain flags, and diversity and coverage were computed against the target classes of interest.

What came back

A screening-ready set with duplicates removed, a documented interference-prone fraction excluded, and a gap analysis showing which target-class chemotypes the collection did not cover.

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 ↗