Integrating ensemble machine learning and multi-omics approaches to identify Dp44mT as a novel anti- agent targeting cellular iron homeostasis.

INTRODUCTION

Candidiasis, mainly caused by , poses a serious threat to human health. The escalating drug resistance in and the limited antifungal options highlight the critical need for novel therapeutic strategies.

METHODS

We evaluated 12 machine learning models on a self-constructed dataset with known anti- activity. Based on their performance, the optimal model was selected to screen our separate in-house compound library with unknown anti- activity for potential antifungal agents. The anti- activity of the selected compounds was confirmed through drug susceptibility assays, hyphal growth assays, and biofilm formation assays. Through transcriptomics, proteomics, iron rescue experiments, CTC staining, JC-1 staining, DAPI staining, molecular docking, and molecular dynamics simulations, we elucidated the mechanism underlying the anti- activity of the compound.

RESULT

Among the evaluated machine learning models, the best predictive model was an ensemble learning model constructed from Random Forests and Categorical Boosting using soft voting. It predicts that Dp44mT exhibits potent anti- activity. The tests further verified this finding that Dp44mT can inhibit planktonic growth, hyphal formation, and biofilm formation of . Mechanistically, Dp44mT exerts antifungal activity by disrupting cellular iron homeostasis, leading to a collapse of mitochondrial membrane potential and ultimately causing apoptosis.

CONCLUSION

This study presents a practical approach for predicting the antifungal activity of com-pounds using machine learning models and provides new insights into the development of antifungal compounds by disrupting iron homeostasis in .