Dienny Rodrigues de Souza, Lívia Do Carmo Silva, Kleber Santiago Freitas E Silva, Fabricio Silva de Jesus, Amanda Alves de Oliveira, Bruno Junior Neves, Maristela Pereira
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引用次数: 0
Abstract
Aims: To evaluate the efficacy of a machine learning approach in developing classification and regression models for antifungal activity against Candida albicans.
Materials & methods: Utilized RF, SVM, and LightGBM algorithms to screen the eMolecules® library. Selected 17 virtual hits for in vitro assays.
Results: Eleven compounds showed activity against C. albicans. Compounds 1 and 17 inhibited C. albicans at 0.51 µM and 0.071 µM, respectively.
Conclusions: The RF model proved effective for virtual screening, demonstrating the success of the physicochemical classification and regression model in identifying new antifungal molecules against C. albicans.
期刊介绍:
Future Microbiology delivers essential information in concise, at-a-glance article formats. Key advances in the field are reported and analyzed by international experts, providing an authoritative but accessible forum for this increasingly important and vast area of research.