文献类型：期刊文献

英文题名：Prediction of Polypharmacological Profiles of Drugs by the Integration of Chemical, Side Effect, and Therapeutic Space

作者：Cheng, Feixiong[1];Li, Weihua[1];Wu, Zengrui[1];Wang, Xichuan[2];Zhang, Chen[1];Li, Jie[1];Liu, Guixia[1];Tang, Yun[1]

机构：[1]E China Univ Sci & Technol, Shanghai Key Lab New Drug Design, Sch Pharm, Shanghai 200237, Peoples R China;[2]Shanghai MCC Hosp, Dept Surg, Shanghai 200941, Peoples R China

年份：2013

卷号：53

期号：4

起止页码：753

外文期刊名：JOURNAL OF CHEMICAL INFORMATION AND MODELING

收录：;EI(收录号：20131816288728);WOS:【SCI-EXPANDED(收录号:WOS:000318060200003)】；

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

语种：英文

外文关键词：Proteins - Tensors - Association reactions - Drug interactions

摘要：Prediction of polypharmacological profiles of drugs enables us to investigate drug side effects and further find their new indications, i.e. drug repositioning, which could reduce the costs while increase the productivity of drug discovery. Here we describe a new computational framework to predict polypharmacological profiles of drugs by the integration of chemical, side effect, and therapeutic space. On the basis of our previous developed drug side effects database, named MetaADEDB, a drug side effect similarity inference (DSESI) method was developed for drug-target interaction (DTI) prediction on a known DTI network connecting 621 approved drugs and 893 target proteins. The area under the receiver operating characteristic curve was 0.882 +/- 0.011 averaged from 100 simulated tests of 10-fold cross-validation for the DSESI method, which is comparative with drug structural similarity inference and drug therapeutic similarity inference methods. Seven new predicted candidate target proteins for seven approved drugs were confirmed by published experiments, with the successful hit rate more than 15.9%. Moreover, network visualization of drug-target interactions and off-target side effect associations provide new mechanism-of-action of three approved antipsychotic drugs in a case study. The results indicated that the proposed methods could be helpful for prediction of polypharmacological profiles of drugs.