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

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 models based on multi-layer perceptron (MLP) and random forest (RF) algorithms to estimate the minimum inhibitory concentration (MIC) of Re complexes against methicillin-resistant (MRSA) and methicillin-sensitive (MSSA) Staphylococcus aureus strains. Utilizing structural descriptors, these models demonstrated strong predictive performance and were successfully applied to evaluate 26 novel Re complexes. Additionally, Shapley additive explanation (SHAP) analysis provided insights into the structural features influencing antibacterial activity predictions. The study's outcomes affirm the effectiveness of our ML-guided approach as a promising pathway for the rational, de novo design of potent Re based antibiotics capable of combating antibiotic-resistant bacterial infections.