Abstract
The risk of methicillin-resistant Staphylococcus aureus (MRSA) infection is increasing in both the developed and developing countries. New approaches to overcome this problem are in need. A ligand-based strategy to discover new inhibiting agents against MRSA infection was built through exploration of machine learning techniques. This strategy is based in two quantitative structure–activity relationship (QSAR) studies, one using molecular descriptors (approach A) and the other using descriptors (approach B). In the approach A, regression models were developed using a total of 6645 molecules that were extracted from the ChEMBL, PubChem and ZINC databases, and recent literature. The performance of the regression models was successfully evaluated by internal and external validation, the best model achieved R2 of 0.68 and RMSE of 0.59 for the test set. In general natural product (NP) drug discovery is a time-consuming process and several strategies for dereplication have been developed to overcome this inherent limitation. In the approach B, we developed a new NP drug discovery methodology that consists in frontloading samples with 1D NMR descriptors to predict compounds with antibacterial activity prior to bioactivity screening for NPs discovery. The NMR QSAR classification models were built using 1D NMR data (1H and 13C) as descriptors, from crude extracts, fractions and pure compounds obtained from actinobacteria isolated from marine sediments collected off the Madeira Archipelago. The overall predictability accuracies of the best model exceeded 77% for both training and test sets.
Highlights
The use as well as misuse of antibiotics in animal feeding and human medicine has resulted in global antimicrobial resistance epidemics [1,2,3]
The whole data set of 6645 small molecules was randomly divided into a training set of 5112 molecules and a test set of 1533 molecules, which were used for the development and external validation of the quantitative structure–activity relationship (QSAR) regression models, respectively
Following our previous work that modeling the anticancer activity against HCT116 [37], the current results suggest as well that the chemoinformatics QSAR approach relying on a ligand-based methodology either based on the molecular structures or the NMR spectra, corroborated with an experimental approach, could be used to predict new inhibitory compounds against methicillin-resistant Staphylococcus aureus (MRSA)
Summary
The use as well as misuse of antibiotics in animal feeding and human medicine has resulted in global antimicrobial resistance epidemics [1,2,3]. The Center for Disease Control and Prevention classifies methicillin-resistant Staphylococcus aureus (MRSA) as a severe threat in health care, leading to more than 80,000 invasive infections and resulting in over 11,000 deaths per year [4]. Mar. Drugs 2019, 17, 16; doi:10.3390/md17010016 www.mdpi.com/journal/marinedrugs. Mar. Drugs 2019, 17, 16 an impressive percentage of patients 60%) acquire MRSA nosocomial infections within 48 h despite having no healthcare issues. New approaches to overcome nosocomial infections and antibiotic resistance are in demand, accelerating the discovery of new antibacterial drugs is highly desirable
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