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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.

The present investigation deals with the antibiotic activity of eight natural guanidine alkaloids and two synthetic analogues against a variety of clinically relevant methicillin-resistant Staphylococcus aureus strains. Galegine (1) and pterogynidine (2) were the most potent compounds, with a minimum inhibitory concentration of 4 mg/L, to all tested strains. The preliminary chemical features correlating to anti-MRSA activity showed that the size of the side chain and the substitution pattern in the guanidine core played a key role in the antibacterial activity of the imino group. Guanidine alkaloids 1 and 2 are promising molecular models for further synthetic derivatives and, thus, for medicinal chemistry studies.