Machine Learning-Based Virtual Screening of Antibacterial Agents against Methicillin-Susceptible and Resistant Staphylococcus aureus.
J Chem Inf Model
; 64(6): 1932-1944, 2024 03 25.
Article
en En
| MEDLINE
| ID: mdl-38437501
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-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.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Staphylococcus aureus Resistente a Meticilina
/
Antibacterianos
Idioma:
En
Revista:
J Chem Inf Model
Asunto de la revista:
INFORMATICA MEDICA
/
QUIMICA
Año:
2024
Tipo del documento:
Article
País de afiliación:
Brasil