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.
Research Center for Drug Discovery and Institute of Human Virology, School of Pharmaceutical Sciences, Sun Yat-Sen University , Guangzhou 510006, China.
Ling
L
Wang
Xiu
X
Le
Long
L
Li
Yingchen
Y
Ju
Zhongxiang
Z
Lin
Qiong
Q
Gu
Jun
J
Xu
D000900:Anti-Bacterial Agents / Q000494:pharmacology*; D003198:Computer Simulation; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D008024:Ligands; D055624:Methicillin-Resistant Staphylococcus aureus / Q000187:drug effects*; D008826:Microbial Sensitivity Tests; D015203:Reproducibility of Results
D000900:Anti-Bacterial Agents; D008024:Ligands