Machine Learning-Based Virtual Screening of Antibacterial Agents against Methicillin-Susceptible and Resistant Staphylococcus aureus
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....
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| Veröffentlicht in: | Journal of chemical information and modeling 2024-03, Vol.64 (6), p.1932-1944 |
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| creator | 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 |
| description | 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. |
| doi_str_mv | 10.1021/acs.jcim.4c00087 |
| format | Article |
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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. 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These compounds showed minimal inhibitory concentrations (MIC) in the range from 12.5 to 200 μM against the different bacterial strains evaluated. 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| 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 |
| title | Machine Learning-Based Virtual Screening of Antibacterial Agents against Methicillin-Susceptible and Resistant Staphylococcus aureus |
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