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

作者：Wang, Ling; Le, Xiu; Li, Long; Ju, Yingchen; Lin, Zhongxiang; Gu, Qiong^*; Xu, Jun

来源：Journal of Chemical Information and Modeling, 2014, 54(11): 3186-3197.

DOI：10.1021/ci500253q

• NAIVE BAYESIAN CLASSIFICATION
• EXTENDED-CONNECTIVITY FINGERPRINTS
• SUPPORT VECTOR MACHINE
• IN-SILICO PREDICTION
• ADME EVALUATION
• INHIBITORS
• MODELS
• FABI
• LIPOPHILICITY
• INFECTIONS

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 naive 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.

• 出版日期2014-11
• 单位南京林业大学; 中山大学