Machine Learning-Based Virtual Screening of Antibacterial Agents against Methicillin-Susceptible and Resistant <i>Staphylococcus aureus</i>
Philipe Oliveira Fernandes, Anna Letícia Teotonio Dias, Valtair Severino dos Santos, Mateus Sá Magalhães Serafim, Yamara Viana Sousa, Gustavo Claro Monteiro, Isabel Duarte Coutinho, Marília Valli, Marina Mol Sena Andrade Verzola, Flaviano Melo Ottoni, Rodrigo Maia de Pádua, Fernando Bombarda Oda, André Gonzaga dos Santos, Adriano D. Andricopulo, Vanderlan da Silva Bolzani, Bruno Eduardo Fernandes Mota, Ricardo José Alves, Renata Barbosa de Oliveira, Thales Kronenberger, Vinícius Gonçalves Maltarollo
Abstract
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.