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Organisms are capable of detecting and fixing nucleotide mismatches that occur during DNA replication. This sophisticated process requires identifying the new strand and replacing the erroneous bases with correct nucleotides. Mismatch repair is coordinated by many proteins in both prokaryotes and eukaryotes.
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Benchmarking Molecular Mutation Operators for Evolutionary Drug Design.

Raúl Acosta Murillo1, Patricio Adrián Zapata-Morin1, José Carlos Ortiz-Bayliss2

  • 1Department of Microbiology and Immunology, School of Biological Sciences, Universidad Autónoma de Nuevo León, Pedro de Alba SN, San Nicolás de los Garza 66455, Nuevo Leon, Mexico.

International Journal of Molecular Sciences
|December 11, 2025
PubMed
Summary
This summary is machine-generated.

Choosing the right molecular mutation strategy is key for AI-driven drug design. Graph-Based Genetic Algorithm offers high validity and efficiency, while others impact molecular complexity and bioactivity differently.

Keywords:
computer-aided drug designgenetic operatorsmolecule mutationmolecule recombination

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Area of Science:

  • Computational chemistry
  • Bioinformatics
  • Drug discovery

Background:

  • Genetic algorithms are powerful tools for drug design.
  • Molecular mutation operators are crucial for exploring chemical space.
  • Optimizing these operators enhances AI-driven drug discovery efficiency.

Purpose of the Study:

  • To compare five molecular mutation strategies for genetic algorithms in drug design.
  • To evaluate their computational efficiency, molecular validity, and impact on complexity.
  • To assess their influence on bioactivity and structural conservation.

Main Methods:

  • Evaluated Graph-Based Genetic Algorithm, Graph-Based Generative Model, SmilesClickChem, SELFIES Token, and SMILES Token Mutation.
  • Assessed computational efficiency, molecular validity, complexity, and structural conservation.
  • Analyzed mutation-induced changes in pIC50 potency and bioactivity.

Main Results:

  • Graph-Based Genetic Algorithm showed the highest molecular validity (96.5%) and efficiency.
  • SmilesClickChem and Graph-Based Generative Model increased molecular complexity.
  • SELFIES Token significantly altered bioactivity, especially for SRC-targeted molecules.

Conclusions:

  • The choice of mutation strategy impacts drug design outcomes, balancing validity, diversity, and cost.
  • Graph-Based Genetic Algorithm is suitable for rapid drug discovery.
  • Findings guide the refinement of evolutionary algorithms for molecular generation and candidate selection.