Machine Learning-Guided Design of Rhenium Tricarbonyl Complexes for Next-Generation Antibiotics.

ABSTRACT: The escalating prevalence of antibiotic-resistant bacteria and the increasing complexity of managing severe infections emphasize the critical need for novel and effective antibiotics. Herein, we present a novel computational strategy focused on metal-based antibiotics, specifically rhenium (Re) complexes, for the rational design of next-generation antibacterial agents. Our approach integrates machine learning (ML) classification models to predict antibacterial potency, particularly against multidrug-resistant pathogens. A recognized limitation of conventional ML-driven antibiotic discovery is its dependence on structural similarity to known antibiotics, which hinders the exploration of structurally diverse and innovative antibiotic classes. To address this, we developed predictive ML model

SUBMITTER: Nedyalkova M 

PROVIDER: S-EPMC12531868 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature