Integrating ensemble machine learning and multi-omics approaches to identify Dp44mT as a novel anti-<i>Candida albicans</i> agent targeting cellular iron homeostasis.

ABSTRACT:

Introduction

Candidiasis, mainly caused by Candida albicans, poses a serious threat to human health. The escalating drug resistance in C. albicans 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-C. albicans activity. Based on their performance, the optimal model was selected to screen our separate in-house compound library with unknown anti-C. albicans activity for potential antifungal agents. The anti-C. albicans activity of the selected compounds was confirmed through in vitro drug susceptibility assays, hyphal growth assays, and biofilm formation assays. Through transcriptomics, proteomics,

SUBMITTER: Chai X 

PROVIDER: S-EPMC12058677 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature