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Related Experiment Videos

Ring systems in mutagenicity databases.

Richard Kho1, Jason A Hodges, Mark R Hansen

  • 1Altoris, Inc., 11575 Sorrento Valley Road, Suite 214, San Diego, CA 92121, USA. rkho@altoris.com

Journal of Medicinal Chemistry
|October 14, 2005
PubMed
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Public mutagenicity databases lack scaffolds common in medicinal chemistry, questioning the utility of predictive models. This study analyzes ring systems to identify mutagenic compound scaffolds and compare them to drug scaffolds.

Area of Science:

  • Computational chemistry
  • Toxicology
  • Medicinal chemistry

Background:

  • Public mutagenicity databases are crucial for assessing chemical safety and developing predictive toxicology models.
  • Understanding the distribution of chemical scaffolds within these databases is essential for model validation and improvement.

Purpose of the Study:

  • To analyze the distribution of ring systems (scaffolds) in public mutagenicity databases.
  • To compare scaffolds found in mutagenicity data with those present in drug datasets.
  • To evaluate the relevance of public mutagenicity data for medicinal chemistry and predictive modeling.

Main Methods:

  • Automated enumeration of substructures to identify and count different ring systems.
  • Statistical analysis using proportions and odds ratios to assess the mutagenicity of specific scaffolds.

Related Experiment Videos

  • Pairwise comparison of odds ratios for isostere replacement studies.
  • Comparison of scaffolds from mutagenicity datasets against those in drug datasets.

    • Main Results:

    • Identification of specific scaffolds frequently occurring in mutagenic compounds.
    • Demonstration that public mutagenicity datasets do not adequately represent scaffolds used in drug discovery.
    • Quantification of the disparity between scaffolds in mutagenicity databases and those in medicinal chemistry.

    Conclusions:

    • The current public mutagenicity databases may not be representative of scaffolds relevant to drug development.
    • The utility of predictive models trained on public mutagenicity data is questionable due to scaffold bias.
    • Automated scaffold analysis methods can be extended to other pharmacological properties for broader chemical insights.