Machine Learning-Based Virtual Screening of Antibacterial Agents against Methicillin-Susceptible and Resistant Staphylococcus aureus

• Published in: Journal of chemical information and modeling Vol. 64; no. 6; pp. 1932 - 1944
• Main Authors: Fernandes, Philipe Oliveira, Dias, Anna Letícia Teotonio, dos Santos Júnior, Valtair Severino, Sá Magalhães Serafim, Mateus, Sousa, Yamara Viana, Monteiro, Gustavo Claro, Coutinho, Isabel Duarte, Valli, Marilia, Verzola, Marina Mol Sena Andrade, Ottoni, Flaviano Melo, Pádua, Rodrigo Maia de, Oda, Fernando Bombarda, dos Santos, André Gonzaga, Andricopulo, Adriano Defini, da Silva Bolzani, Vanderlan, Mota, Bruno Eduardo Fernandes, Alves, Ricardo José, de Oliveira, Renata Barbosa, Kronenberger, Thales, Maltarollo, Vinícius Gonçalves
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 25.03.2024
• Subjects: Anti-Bacterial Agents - pharmacology; Machine learning; Machine Learning and Deep Learning; Methicillin - pharmacology; Methicillin-Resistant Staphylococcus aureus; Microbial Sensitivity Tests; Pathogens; Pharmacology; Staphylococcus aureus; Workflow
• ISSN: 1549-9596; 1549-960X; 1549-960X
• DOI: 10.1021/acs.jcim.4c00087

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-resistantStaphylococcus 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.