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Evolving interpretable structure-activity relationship models. 2. Using multiobjective optimization to derive

Kristian Birchall1, Valerie J Gillet, Gavin Harper

  • 1Department of Information Studies, University of Sheffield, Regent Court, 211 Portobello Street, Sheffield, United Kingdom.

Journal of Chemical Information and Modeling
|July 22, 2008
PubMed
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This study introduces a multiobjective evolutionary algorithm (MOEA) to evolve multiple structure-activity relationships (SARs). The MOEA optimizes recall and precision simultaneously, generating diverse SARs for better compound analysis.

Area of Science:

  • Computational Chemistry
  • Cheminformatics
  • Machine Learning

Background:

  • Traditional machine learning methods often involve a precision-recall tradeoff, limiting control over model behavior.
  • Existing approaches typically combine precision and recall into a single metric, sacrificing nuanced control.
  • Identifying multiple, distinct structure-activity relationships (SARs) within complex datasets is challenging.

Purpose of the Study:

  • To develop a multiobjective evolutionary algorithm (MOEA) for evolving multiple structure-activity relationships (SARs).
  • To address the inherent precision-recall tradeoff in machine learning for SAR discovery.
  • To enable users to select SARs based on desired precision-recall balances for different applications.

Main Methods:

  • Encoding SARs as interpretable reduced graph queries.

Related Experiment Videos

  • Simultaneously optimizing recall and precision using a multiobjective evolutionary algorithm (MOEA).
  • Forming teams of complementary queries by incorporating uniqueness as a third objective post-evolution.

    • Main Results:

    • Generation of a family of SARs represented on a precision-recall (PR) curve, offering user-selectable balances.
    • Demonstration of improved recall and precision for certain datasets through the MOEA approach.
    • Development of methods to capture multiple, potentially co-existing SARs within a dataset.

    Conclusions:

    • The MOEA provides a flexible framework for SAR discovery, allowing tailored selection of queries based on specific needs (e.g., SAR establishment vs. virtual screening).
    • Combining individual queries into teams enhances the ability to represent complex structure-activity information, especially from high-throughput screening data.
    • The inclusion of uniqueness ensures that evolved queries are complementary, leading to more comprehensive structure-activity insights.