Employing machine learning for identifying antifungal compounds against Candida albicans.

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.