Discovering New Agents Active against Methicillin-Resistant Staphylococcus aureus with Ligand-Based Approaches

To discover new agents active against
methicillin-resistant Staphylococcus aureus (MRSA), in silico models derived from 5451 cell-based
anti-MRSA assay data were developed
using four machine learning methods, including naïve Bayesian,
support vector machine (SVM), recursive partitioning (RP), and k-nearest
neighbors (kNN). A total of 876 models have been constructed based
on physicochemical descriptors and fingerprints. The overall predictive
accuracies of the best models exceeded 80% for both training and test
sets. The best model was employed for the virtual screening of anti-MRSA
compounds, which were then validated by a cell-based assay using the
broth microdilution method with three types of highly resistant MRSA
strains (ST239, ST5, and 252). A total of 12 new anti-MRSA agents
were confirmed, which had MIC values ranging from 4 to 64 mg/L. This
work proves the capacity of combined multiple ligand-based approaches
for the discovery of new agents active against MRSA with cell-based
assays. We think this work may inspire other lead identification processes
when cell-based assay data are available.