文献类型：期刊文献

英文题名：Classification of Cytochrome P450 Inhibitors and Noninhibitors Using Combined Classifiers

作者：Cheng, Feixiong[1];Yu, Yue[1];Shen, Jie[1];Yang, Lei[3];Li, Weihua[1];Liu, Guixia[1];Lee, Philip W.[1,2];Tang, Yun[1]

机构：[1]E China Univ Sci & Technol, Sch Pharm, Dept Pharmaceut Sci, Shanghai 200237, Peoples R China;[2]Kyoto Univ, Grad Sch Agr, Sakyo Ku, Kyoto 6068502, Japan;[3]E China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China

年份：2011

卷号：51

期号：5

起止页码：996

外文期刊名：JOURNAL OF CHEMICAL INFORMATION AND MODELING

收录：;EI(收录号：20112914149624);WOS:【SCI-EXPANDED(收录号:WOS:000290747300003)】；

基金：We thank Dr. Douglas Auld (Genomic Assay Technologies, NIH Chemical Genomics Center, USA) in sharing their data sets with us. This work was supported by the National Natural Science Foundation of China (Grant No. 21072059), Shanghai Natural Science Foundation (Grant No. 10ZR1407000), the Fundamental Research Funds for the Central Universities (Grant No. WY1014010), the Program for New Century Excellent Talents in University (Grant No. NCET-08-0774), the 111 Project (Grant No. B07023), and the National S&T Major Project of China (Grant No. 2009ZX09501-001).

语种：英文

外文关键词：Decision trees - Data mining - Nearest neighbor search - Classification (of information) - Drug interactions - Backpropagation - Forecasting

摘要：Adverse side effects of drug drug interactions induced by human cytochrome P450 (CYP) inhibition is an important consideration, especially, during the research phase of drug discovery. It is highly desirable to develop computational models that can predict the inhibitive effect of a compound against a specific CYP isoform. In this study, inhibitor predicting models were developed for five major CYP isoforms, namely 1A2, 2C9, 2C19, 2D6, and 3A4, using a combined classifier algorithm on a large data set containing more than 24,700 unique compounds, extracted from PubChem. The combined classifiers algorithm is an ensemble of different independent machine learning classifiers including support vector machine, C4.5 decision tree, k-nearest neighbor, and naive Bayes, fused by a back-propagation artificial neural network (BP-ANN). All developed models were validated by 5-fold cross-validation and a diverse validation set composed of about 9000 diverse unique compounds. The range of the area under the receiver operating characteristic curve (AUC) for the validation sets was 0.764 to 0.815 for CYP1A2, 0.837 to 0.861 for CYP2C9, 0.793 to 0.842 for CYP2C19, 0.839 to 0.886 for CYP2D6, and 0.754 to 0.790 for CYP3A4, respectively, using the new developed combined classifiers. The overall performance of the combined classifiers fused by BP-ANN was superior to that of three classic fusion techniques (Mean, Maximum, and Multiply). The chemical spaces of data sets were explored by multidimensional scaling plots, and the use of applicability domain improved the prediction accuracies of models. In addition, some representative substructure fragments differentiating CYP inhibitors and noninhibitors were characterized by the substructure fragment analysis. These classification models are applicable for virtual screening of the five major CYP isoforms inhibitors or can be used as simple filters of potential chemicals in drug discovery.