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

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• 作者：Ling Wang ; Xiu Le ; Long Li ; Yingchen Ju ; Zhongxiang Lin ; Qiong Gu ; Jun Xu
• 刊名：Journal of Chemical Information and Modeling
• 出版年：2014
• 出版时间：November 24, 2014
• 年：2014
• 卷：54
• 期：11
• 页码：3186-3197
• 全文大小：541K
• ISSN：1549-960X

文摘

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 na茂ve 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.