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  1. Home
  3. Interpretable And Scalable Similarity Metrics For Dna-encoded Library Design Using Generative Topographic Mapping.
  1. Home
  3. Interpretable And Scalable Similarity Metrics For Dna-encoded Library Design Using Generative Topographic Mapping.

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Interpretable and Scalable Similarity Metrics for DNA-Encoded Library Design Using Generative Topographic Mapping.

Louis Plyer1, Alexey A Orlov1, Tagir N Akhmetshin1

  • 1Laboratory of Chemoinformatics, UMR 7140 CNRS, University of Strasbourg, Strasbourg, France.

Molecular Informatics
|March 26, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Generative Topographic Mapping (GTM) effectively selects DNA-encoded libraries (DELs) by approximating traditional metrics. This approach ensures selected DELs are reference-proximal and diverse, aiding drug discovery.

Keywords:
Generative Topographic Mappingchemical librarieschemical spacechemographydimensionality reduction

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Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Cheminformatics

Background:

  • DNA-encoded libraries (DELs) are expanding, necessitating efficient selection criteria.
  • Selecting optimal DELs requires balancing similarity to active compounds and intra-library diversity.
  • Existing methods for DEL selection lack scalability and interpretability.

Purpose of the Study:

  • To evaluate Generative Topographic Mapping (GTM) as a scalable and interpretable method for selecting DNA-encoded libraries (DELs).
  • To compare GTM-derived "stand-alone" (SA) metrics with conventional compound pair-matching (CP) metrics for DEL selection.
  • To assess GTM's ability to identify DELs that are both reference-proximal and chemically diverse.

Main Methods:

  • Comparative analysis of Morgan count fingerprint-based CP metrics and GTM-derived SA metrics.
  • Utilized 100 diverse DEL subsets and a ChEMBL reference set for cyclin-dependent kinase 2 (CDK2) screening.
  • Calculated Spearman rank correlations and enrichment factors (EF5%) to assess metric performance.

    • Main Results:

    • GTM-based SA metrics demonstrated robust approximations of gold standard CP metrics, with Spearman correlations ranging from 0.6-0.7.
    • GTM successfully identified DELs that best span the reference space, achieving EF5% values of 4-12.
    • GTM consistently selected top-performing libraries, identifying 2 out of the top 3 gold standard libraries within the top 5% ranked by GTM.
    • Two-dimensional GTM landscapes provided visual assessment of intra- and inter-library diversity.

    Conclusions:

    • GTM serves as an efficient tool for chemical library similarity assessment and target-focused DEL selection.
    • GTM offers a scalable and interpretable alternative to traditional methods for designing and screening DELs.
    • The visual outputs of GTM support rapid evaluation of diverse DEL designs for drug discovery.