Accelerating antibiotic discovery by leveraging machine learning models: Application to identify novel inorganic complexes

The expanded prevalence of resistant bacteria and the inherent challenges of complicated infections highlight the urgent need to develop credible antibiotic options. Through conventional screening approaches, the discovery of new antibiotics has proven to be challenging. Anti-infective drugs, including antibacterials, antivirals, antifungals, and antiparasitics, have become less effective due to the spread of drug resistance. In this work we help define the design of next-generation antibiotic analogs based on metal complexes. The primary direction is based on the application of artificial intelligence (AI) methods, which demonstrated superior ability in tackling resistance in Gram-positive and Gram-negative bacteria, including multidrug-resistant strains. The bottleneck of the existing AI approaches relies on the structure similarities of the current antibiotics. The question of discovering and developing new unconventional antibiotic classes has challenged preconceptions about the scope and applicability of the existing methods. Herein, we developed a machine learning approach that predicts the minimum inhibitory concentration (MIC) of Re-complexes towards two S. aureus strains (ATCC 43300 - MRSA and ATCC 25923 - MSSA). Multi-layer Perceptron (MLP) was tailored with the structure features of the Re-complexes to develop the prediction model. Although our approach is demonstrated with a specific example, based on the rhenium carbonyl complexes, the predictive model can be readily adjusted to other candidate metal complexes. The model emphasizes applying a developed approach in the de novo design of a metal-based antibiotic with targeted activity against a challenging pathogen.