Prediction of Polypharmacological Profiles of Drugs by the Integration of Chemical, Side Effect, and Therapeutic Space

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• 作者：Feixiong Cheng ; Weihua Li ; Zengrui Wu ; Xichuan Wang ; Chen Zhang ; Jie Li ; Guixia Liu ; Yun Tang
• 刊名：Journal of Chemical Information and Modeling
• 出版年：2013
• 出版时间：April 22, 2013
• 年：2013
• 卷：53
• 期：4
• 页码：753-762
• 全文大小：758K
• 年卷期：v.53,no.4(April 22, 2013)
• ISSN：1549-960X

文摘

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鈥搕arget 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 卤 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鈥搕arget 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.