Machine Learning-Based Virtual Screening of Antibacterial Agents against Methicillin-Susceptible and Resistant Staphylococcus aureus .
Philipe de Olveira FernandesAnna Letícia Teotonio DiasValtair Severino Dos Santos JúniorMateus Sá Magalhães SerafimYamara Viana SousaGustavo Claro MonteiroIsabel Duarte CoutinhoMarilia ValliMarina Mol Sena Andrade VerzolaFlaviano Melo OttoniRodrigo Maia de PáduaFernando Bombarda OdaAndré Gonzaga Dos SantosAdriano Defini AndricopuloVanderlan da Silva BolzaniBruno Eduardo Fernandes MotaRicardo José AlvesRenata Barbosa de OliveiraThales KronenbergerVinícius Gonçalves MaltarolloPublished in: Journal of chemical information and modeling (2024)
The application of computer-aided drug discovery (CADD) approaches has enabled the discovery of new antimicrobial therapeutic agents in the past. The high prevalence of methicillin-resistant Staphylococcus aureus (MRSA) strains promoted this pathogen to a high-priority pathogen for drug development. In this sense, modern CADD techniques can be valuable tools for the search for new antimicrobial agents. We employed a combination of a series of machine learning (ML) techniques to select and evaluate potential compounds with antibacterial activity against methicillin-susceptible S. aureus (MSSA) and MRSA strains. In the present study, we describe the antibacterial activity of six compounds against MSSA and MRSA reference (American Type Culture Collection (ATCC)) strains as well as two clinical strains of MRSA. These compounds showed minimal inhibitory concentrations (MIC) in the range from 12.5 to 200 μM against the different bacterial strains evaluated. Our results constitute relevant proven ML-workflow models to distinctively screen for novel MRSA antibiotics.