文献类型：期刊文献

英文题名：Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference

作者：Cheng, Feixiong[1];Liu, Chuang[2];Jiang, Jing[1];Lu, Weiqiang[1];Li, Weihua[1];Liu, Guixia[1];Zhou, Weixing[2];Huang, Jin[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;[2]E China Univ Sci & Technol, Sch Business, Shanghai 200237, Peoples R China

年份：2012

卷号：8

期号：5

外文期刊名：PLOS COMPUTATIONAL BIOLOGY

收录：;EI(收录号：20223012433638);WOS:【SCI-EXPANDED(收录号:WOS:000305964600013)】；

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), the Innovation Program of Shanghai Municipal Education Commission (Grant 10ZZ41), and the Shanghai Committee of Science and Technology (Grant 11DZ2260600). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

语种：英文

外文关键词：Cell culture - Complex networks - Computation theory - Drug interactions - Tensors

摘要：Drug-target interaction (DTI) is the basis of drug discovery and design. It is time consuming and costly to determine DTI experimentally. Hence, it is necessary to develop computational methods for the prediction of potential DTI. Based on complex network theory, three supervised inference methods were developed here to predict DTI and used for drug repositioning, namely drug-based similarity inference (DBSI), target-based similarity inference (TBSI) and network-based inference (NBI). Among them, NBI performed best on four benchmark data sets. Then a drug-target network was created with NBI based on 12,483 FDA-approved and experimental drug-target binary links, and some new DTIs were further predicted. In vitro assays confirmed that five old drugs, namely montelukast, diclofenac, simvastatin, ketoconazole, and itraconazole, showed polypharmacological features on estrogen receptors or dipeptidyl peptidase-IV with half maximal inhibitory or effective concentration ranged from 0.2 to 10 mu M. Moreover, simvastatin and ketoconazole showed potent antiproliferative activities on human MDA-MB-231 breast cancer cell line in MTT assays. The results indicated that these methods could be powerful tools in prediction of DTIs and drug repositioning.