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Creating the New from the Old: Combinatorial Libraries Generation with Machine-Learning-Based Compound Structure

Sabina Podlewska1, Wojciech M Czarnecki2, Rafał Kafel1

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This study introduces a novel method for generating targeted compound libraries using machine learning and substructure optimization. This approach enhances virtual screening for discovering new drug candidates with improved activity and accessibility.

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Area of Science:

  • Computational chemistry
  • Cheminformatics
  • Drug discovery

Background:

  • Virtual screening is popular in drug design, often using commercial databases or in silico libraries.
  • Combinatorial approaches generate new molecules based on chemical structures and substructures.

Purpose of the Study:

  • To develop a machine learning-driven method for generating targeted combinatorial libraries.
  • To optimize substructural composition for enhanced ligand activity against specific protein targets.

Main Methods:

  • Utilized a machine learning optimization procedure to identify optimal substructural fingerprints.
  • Generated combinatorial libraries through systematic enumeration of preferred substructures.
  • Assessed generated compounds using machine learning (Klekota-Roth fingerprint, support vector machine), physicochemical properties, and synthetic accessibility.

Main Results:

  • Identified Klekota-Roth fingerprint and support vector machine as an optimal combination for virtual screening.
  • Generated target-focused libraries for 8 protein targets.
  • Characterized physicochemical properties and synthetic accessibility of the novel compounds.

Conclusions:

  • The developed method enables the creation of focused compound libraries for drug discovery.
  • The study provides a valuable resource of potential ligands and their characterization for researchers.
  • Machine learning-based substructure optimization is effective for in silico drug design.