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

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(3), P. 1166 - 1166

Published: Jan. 24, 2025

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

Language: Английский

Identification of Natural Compounds as Inhibitors of Clumping Factor A in staphylococcus aureus to Combat Bovine Mastitis: An in-Silico Approach

Biocatalysis and Agricultural Biotechnology, Journal Year: 2025, Volume and Issue: unknown, P. 103556 - 103556

Published: March 1, 2025

Language: Английский