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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.

NuBBE DB is the first library of natural products of Brazilian biodiversity. It includes a large variety of classes of compounds and structural types of secondary metabolites of plants, fungi, insects, marine organisms, and bacteria. So far the chemical diversity and complexity of NuBBE DB have not been characterized in a systematic and detailed manner. Herein, we report a comprehensive chemoinformatic analysis of the most current version of NuBBE DB . As part of the characterization, NuBBE DB was compared with several databases of natural products in terms of structural diversity and complexity. Results of the analysis showed that NuBBE DB is diverse in terms of structural fingerprints, distribution of chemical scaffolds, and molecular properties. In addition, the results of the visualization of chemical space support quantitatively that NUBBE DB is a promising source of molecules for drug discovery and medicinal chemistry.