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  1. Home
  3. Dna-encoded Chemical Library Screening With Target Titration Analysis: Delta.
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  3. Dna-encoded Chemical Library Screening With Target Titration Analysis: Delta.

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DNA-Encoded Chemical Library Screening with Target Titration Analysis: DELTA.

John C Faver1, Flora Sundersingh1, Lauren A Viarengo-Baker1

  • 1Relay Therapeutics, 399 Binney Street, Cambridge, Massachusetts 02141, United States.

Journal of Medicinal Chemistry
|January 2, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

DNA-encoded chemical libraries (DELs) offer efficient screening but yield noisy data. A new split-sample DEL strategy improves binding affinity ranking and identifies potent compounds missed by standard methods.

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Area of Science:

  • Medicinal Chemistry
  • Biochemistry
  • Computational Chemistry

Background:

  • DNA-encoded chemical libraries (DELs) are powerful tools for screening vast numbers of small molecules against biological targets.
  • DEL screening data often suffer from noise due to variations in synthetic yields, complicating hit identification.
  • Machine learning models benefit from DEL data for virtual screening, but data quality is a key challenge.

Purpose of the Study:

  • To develop and validate a novel split-sample DNA-encoded chemical library screening strategy.
  • To improve the accuracy of binding affinity estimation and hit compound ranking in DEL screening.
  • To address the challenge of noisy data and enhance the detection of high-affinity binders.

Main Methods:

  • A split-sample DEL screening approach was employed against Bruton's tyrosine kinase (BTK).
  • Affinity selections were performed at varying target concentrations.
  • A probabilistic model was used to estimate binding affinity and relative library member concentrations.
  • Model predictions were validated against Surface Plasmon Resonance (SPR) measurements of resynthesized compounds.

    • Main Results:

    • The developed methodology provided an improved ranking of library members by binding affinity compared to traditional enrichment metrics.
    • The probabilistic model successfully estimated binding affinities and relative input concentrations.
    • A highly potent binding compound, previously undetectable, was recovered using this enhanced method.
    • Validation against SPR confirmed the improved accuracy of the model's predictions.

    Conclusions:

    • The split-sample DEL screening strategy with a probabilistic model significantly enhances the reliability of hit identification.
    • This approach mitigates the impact of synthetic yield variations, leading to more accurate binding affinity assessments.
    • The methodology offers a more sensitive and robust way to discover potent drug candidates from large chemical libraries.