文献类型：期刊文献

英文题名：Prediction of chemical-protein interactions: multitarget-QSAR versus computational chemogenomic methods

作者：Cheng, Feixiong[1];Zhou, Yadi[1];Li, Jie[1];Li, Weihua[1];Liu, Guixia[1];Tang, Yun[1]

机构：[1]E China Univ Sci & Technol, Sch Pharm, Shanghai Key Lab New Drug Design, Shanghai 200237, Peoples R China

年份：2012

卷号：8

期号：9

起止页码：2373

外文期刊名：MOLECULAR BIOSYSTEMS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000307014300016)】；

基金：This work was supported by the 863 Project (Grant 2012AA020308), the National Natural Science Foundation of China (Grant 21072059), the 111 Project (Grant B07023), the Fundamental Research Funds for the Central Universities (Grant WY1113007), and the Shanghai Committee of Science and Technology (Grant 11DZ2260600). We thank the anonymous reviewers for their valuable suggestions.

语种：英文

摘要：Elucidation of chemical-protein interactions (CPI) is the basis of target identification and drug discovery. It is time-consuming and costly to determine CPI experimentally, and computational methods will facilitate the determination of CPI. In this study, two methods, multitarget quantitative structure-activity relationship (mt-QSAR) and computational chemogenomics, were developed for CPI prediction. Two comprehensive data sets were collected from the ChEMBL database for method assessment. One data set consisted of 81 689 CPI pairs among 50 924 compounds and 136 G-protein coupled receptors (GPCRs), while the other one contained 43 965 CPI pairs among 23 376 compounds and 176 kinases. The range of the area under the receiver operating characteristic curve (AUC) for the test sets was 0.95 to 1.0 and 0.82 to 1.0 for 100 GPCR mt-QSAR models and 100 kinase mt-QSAR models, respectively. The AUC of 5-fold cross validation were about 0.92 for both 176 kinases and 136 GPCRs using the chemogenomic method. However, the performance of the chemogenomic method was worse than that of mt-QSAR for the external validation set. Further analysis revealed that there was a high false positive rate for the external validation set when using the chemogenomic method. In addition, we developed a web server named CPI-Predictor, http://www.lmmd.org/online_services/cpi_predictor/, which is available for free. The methods and tool have potential applications in network pharmacology and drug repositioning.