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Related Experiment Videos

Learning from the data: mining of large high-throughput screening databases.

S Frank Yan1, Frederick J King, Yun He

  • 1Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, California 92121, USA. syan@gnf.org

Journal of Chemical Information and Modeling
|November 28, 2006
PubMed
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We developed an ontology-based pattern identification (OPI) algorithm to mine high-throughput screening (HTS) data. This method reliably identifies scaffold families with significant structure-activity relationships, distinguishing between true biological activity and screening artifacts.

Area of Science:

  • Drug discovery and development
  • Computational chemistry
  • Bioinformatics

Background:

  • Pharmaceutical companies possess vast high-throughput screening (HTS) datasets from millions of compounds.
  • Systematic data mining methods to extract actionable knowledge from HTS data are underdeveloped.
  • Understanding structure-activity relationships is crucial for drug development.

Purpose of the Study:

  • To develop and apply a systematic data mining approach for extracting knowledge from HTS data.
  • To identify compound scaffolds with statistically significant structure-activity profiles.
  • To differentiate between genuine biological activity and screening artifacts.

Main Methods:

  • Development of an ontology-based pattern identification (OPI) algorithm.

Related Experiment Videos

  • Application of OPI to an in-house high-throughput screening database.
  • Utilizing statistical tests (Kruskal-Wallis, ANOVA) for scaffold analysis.

    • Main Results:

    • Identified nearly 1500 scaffold families with significant structure-HTS activity profile relationships.
    • Characterized dozens of scaffolds as artifacts related to screening technology.
    • Classified compound scaffolds into four types: tumor cytotoxic, general toxic, reporter gene assay artifact, and target family specific.

    Conclusions:

    • The OPI approach reliably identifies compounds with similar structures and shared biological activity profiles.
    • Discovered scaffolds are valuable for designing diversity libraries and in silico biological annotations.
    • This method provides novel target family-specific scaffolds for focused library design.