Discovery of novel polymyxin E adjuvants against <i>Acinetobacter baumannii</i> guided by a stacking-based machine learning model.

ABSTRACT: The rapid spread of antibiotic resistance poses an escalating global health crisis. Polymyxin E, the antibiotic of last resort, is limited by dose-dependent toxicity. Antibiotic adjuvants offer a promising strategy to enhance efficacy while reducing dosages, potentially mitigating this critical issue. We conducted antibiotic potentiation assays on 1245 proprietary compounds, characterized using a hybrid PubChem-MACCS molecular fingerprint approach. Following evaluation of multiple machine learning models, we developed a stacking model, ADStack, combining random forest and extreme gradient boosting algorithms. Using 0.05 as the p-value cutoff, ADStack significantly outperformed the base-classifier RF in terms of AUC on the test set. While no significant difference was observed compar

SUBMITTER: Chen Y 

PROVIDER: S-EPMC12780549 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature