Employing machine learning for identifying antifungal compounds against .

Journal: Future microbiology
Published Date: Jul 2, 2025

Abstract

AIMS: To evaluate the efficacy of a machine learning approach in developing classification and regression models for antifungal activity against .

Authors

  • Dienny Rodrigues de Souza
    Laboratory of Molecular Biology, Institute of Biological Sciences, Federal University of Goiás, Goiânia, Brazil.
  • Lívia Do Carmo Silva
    Laboratory of Molecular Biology, Institute of Biological Sciences, Federal University of Goiás, Goiânia, Brazil.
  • Kleber Santiago Freitas E Silva
    Laboratory of Molecular Biology, Institute of Biological Sciences, Federal University of Goiás, Goiânia, Brazil.
  • Fabricio Silva de Jesus
    Laboratory of Cheminformatics, Faculty of Pharmacy, Federal University of Goiás, Goiânia, Brazil.
  • Amanda Alves de Oliveira
    Laboratory of Molecular Biology, Institute of Biological Sciences, Federal University of Goiás, Goiânia, Brazil.
  • Bruno Junior Neves
    LabChem - Laboratory of Cheminformatics, Centro Universitário de Anápolis, UniEVANGÉLICA, Anápolis, GO, 75083-515, Brazil; LabMol - Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás, Goiânia, GO, 74605-510, Brazil.
  • Maristela Pereira
    Laboratory of Molecular Biology, Institute of Biological Sciences, Federal University of Goiás, Goiânia, Brazil.

Keywords

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