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Updated: Jun 28, 2026

High-throughput Screening for Chemical Modulators of Post-transcriptionally Regulated Genes
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Published on: March 3, 2015

Dealing with the data deluge in high throughput screening.

P Skehan1

  • 1Andes Pharmaceuticals, Inc., 26520 Northeast 15th Street, Redmond, WA 98053, USA.

Journal of Automated Methods & Management in Chemistry
|October 18, 2008
PubMed
Summary
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Numerical taxonomy and pattern recognition streamline drug discovery by reducing assay needs. This approach identifies effective drug leads early, saving significant resources in development.

Area of Science:

  • Computational chemistry
  • Bioinformatics
  • Drug discovery

Background:

  • Multiple-assay screening programs generate extensive data.
  • Efficiently analyzing this data is crucial for drug discovery.
  • Identifying promising drug candidates early minimizes later-stage failures.

Purpose of the Study:

  • To demonstrate how numerical taxonomy and pattern recognition can reduce data burden in screening.
  • To outline a three-stage discovery process for improved efficiency.
  • To enhance the selection of drug leads with higher success potential.

Main Methods:

  • Application of numerical taxonomy and pattern recognition techniques.
  • Rational design of prescreens.
  • Selection of reporter assays based on chemical response patterns.

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Last Updated: Jun 28, 2026

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  • Integration of clinical failure assays into the screening process.

    • Main Results:

    • Reduced information burden in multiple-assay screening.
    • Identification of assays with similar chemical response patterns.
    • Effective evaluation of drug selectivity and mechanism of action prediction.
    • Significant reduction (over 20-fold) in required culture wells.
    • Identification of 1-2 high-potential drug leads per 1000 tested.

    Conclusions:

    • Numerical taxonomy and pattern recognition are powerful tools for optimizing drug discovery.
    • A streamlined three-stage screening process enhances efficiency and lead quality.
    • This methodology significantly reduces the number of compounds requiring further development.