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

• Published in: Journal of chemical information and modeling 2014-11, Vol.54 (11), p.3186-3197
• Main Authors: Wang, Ling, Le, Xiu, Li, Long, Ju, Yingchen, Lin, Zhongxiang, Gu, Qiong, Xu, Jun
• Format: Article
• Language: English
• Subjects: Anti-Bacterial Agents - pharmacology; Artificial intelligence; Bayesian analysis; Cells; Computer Simulation; Drug Evaluation, Preclinical - methods; Ligands; Methicillin-Resistant Staphylococcus aureus - drug effects; Microbial Sensitivity Tests; Reproducibility of Results; Staphylococcus infections
• ISSN: 1549-9596; 1549-960X
• DOI: 10.1021/ci500253q

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.