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Selector: A General Python Library for Diverse Subset Selection.

Fanwang Meng1,2, Marco Martínez González2, Valerii Chuiko2

  • 1Department of Chemistry, Queen's University, 90 Bader Lane, Kingston, Ontario K7L 3N6, Canada.

Journal of Chemical Information and Modeling
|January 27, 2026
PubMed
Summary
This summary is machine-generated.

Selector is a free, open-source Python library for diverse subset selection. It offers various algorithms and integrates with Scikit-Learn for data analysis, chemistry, and drug discovery applications.

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Area of Science:

  • Data Science
  • Computational Chemistry
  • Bioinformatics

Background:

  • Selecting diverse subsets is crucial for efficient data analysis and discovery.
  • Existing tools may lack flexibility or comprehensive algorithms for diverse subset selection.

Purpose of the Study:

  • Introduce Selector, a free, open-source Python library for diverse subset selection.
  • Provide a versatile tool for various application domains, including computational chemistry and drug discovery.

Main Methods:

  • Implemented diverse subset sampling algorithms based on distance, similarity, and spatial partitioning.
  • Integrated with popular Python libraries like Scikit-Learn for seamless workflow compatibility.
  • Developed interactive notebooks and a web interface for user accessibility.

Main Results:

  • Selector offers a flexible and extensible package with modern software development practices.
  • Demonstrated interoperability with existing data analysis workflows.
  • Showcased applications in computational chemistry, drug discovery, and chemical library design through user-friendly tutorials.

Conclusions:

  • Selector provides a robust and accessible solution for diverse subset selection across multiple scientific domains.
  • The library's design ensures quality, maintainability, and ease of use for researchers of all skill levels.