Future microbiology · 2025
Employing machine learning for identifying antifungal compounds against Candida albicans.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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de Souza Dienny Rodrigues, Silva Lívia Do Carmo, E Silva Kleber Santiago Freitas, de Jesus Fabricio Silva, de Oliveira Amanda Alves, Neves Bruno Junior, Pereira Maristela
Аннотация
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.
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.