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Comparison of algorithms for dissimilarity-based compound selection

M Snarey1, N K Terrett, P Willett

  • 1Pfizer Central Research, Sandwich, Kent, United Kingdom.

Journal of Molecular Graphics & Modelling
|August 15, 1998
PubMed
Summary
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Maximum dissimilarity and sphere-exclusion algorithms effectively select diverse compound subsets. The MaxMin algorithm demonstrated superior effectiveness and efficiency in identifying biological activity classes and active compounds from databases.

Area of Science:

  • Computational chemistry
  • Cheminformatics
  • Drug discovery

Background:

  • Dissimilarity-based compound selection is crucial for creating diverse chemical libraries.
  • Selecting structurally diverse subsets enhances the efficiency of virtual screening and drug discovery efforts.
  • Various algorithms exist for maximizing structural diversity in compound selection.

Purpose of the Study:

  • To compare the effectiveness of different maximum-dissimilarity and sphere-exclusion algorithms.
  • To evaluate these algorithms for selecting structurally diverse compound subsets from chemical databases.
  • To quantify algorithm performance using biological activity class identification and feedback search metrics.

Main Methods:

  • Implemented and compared several maximum-dissimilarity algorithms.

Related Experiment Videos

  • Implemented and compared several sphere-exclusion algorithms.
  • Tested algorithms on the World Drugs Index database, quantifying results by biological activity classes and active compound identification in feedback searches.

    • Main Results:

    • The MaxMin algorithm demonstrated general effectiveness in dissimilarity-based compound selection.
    • Experiments quantified the number of biological activity classes identified in selected subsets.
    • Feedback searches confirmed the identification of active compounds, highlighting algorithm efficiency.

    Conclusions:

    • The MaxMin algorithm is an effective and efficient method for dissimilarity-based compound selection.
    • This approach aids in identifying structurally diverse subsets for drug discovery.
    • The study validates the utility of dissimilarity-based methods for chemical database subsetting.