Perturbation-Theory Machine Learning for Multi-Objective Antibacterial Discovery: Current Status and Future Perspectives

Antibacterial drugs (commonly known as antibiotics) are essential for eradicating bacterial infections. Nowadays, antibacterial discovery has become an imperative need due to the lack of efficacious antibiotics, the ever-increasing development of multi-drug resistance (MDR), and the withdrawal of many pharmaceutical industries from antibacterial discovery programs. Currently, drug discovery is widely recognized as a multi-objective optimization problem where computational approaches could play a pivotal role, enabling the identification of novel and versatile antibacterial agents. Yet, tackling complex phenomena such as the multi-genic nature of bacterial infections and MDR is a major disadvantage of most of the modern computational methods. To the best of our knowledge, perturbation-theory machine learning (PTML) appears to be the only computational approach capable of overcoming the aforementioned limitation. The present review discusses PTML modeling as the most suitable cutting-edge computational approach for multi-objective optimization in antibacterial discovery. In this sense, we focus our attention on the development and application of PTML models for the prediction and/or design of multi-target (multi-protein or multi-strain) antibacterial inhibitors in the context of small organic molecules, peptide design, and metal-containing nanoparticles. Additionally, we highlight future applications of PTML modeling in the context of novel drug-like chemotypes with multi-protein and/or multi-strain antibacterial activity.