Machine Learning-Guided Design of High-Performance Antifungal Polymers against <i>Candida albicans</i>.

ABSTRACT: This study leverages machine learning (ML) to accelerate the development of antifungal synthetic polymers. An in-house curated data set was used to train a Random Forest binary classification model, which identified five critical features for predicting antifungal activity against the fungal pathogen Candida albicans. This model shows that antifungal polymers should contain a hydrophilic composition of at least 30%, a calculated partition coefficient (cLogP) of 0 to +0.5, hydrophobic composition limited to 20%, degree of polymerization (DP) of 18 or less, and cationic composition limited to 50% for the polymer to have an MIC₉₀ of 64 μg/mL or less. Based on these insights, a library of polymers was synthesized via reversible addition-fragmentation chain transfer (RAFT) pol

SUBMITTER: Roh J 

PROVIDER: S-EPMC12511976 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature