文献类型：期刊文献

英文题名：SDTNBI: an integrated network and chemoinformatics tool for systematic prediction of drug-target interactions and drug repositioning

作者：Wu, Zengrui[5];Cheng, Feixiong[1,2,3];Li, Jie[5];Li, Weihua[5];Liu, Guixia[5];Tang, Yun[4]

机构：[1]Dana Farber Canc Inst, Ctr Canc Syst Biol CCSB, Harvard Med Sch, 450 Brookline Ave, Boston, MA 02215 USA;[2]Northeastern Univ, Ctr Complex Networks Res, 110 Forsyth St, Boston, MA 02115 USA;[3]Sichuan Univ, West China Hosp, West China Med Sch, State Key Lab Biotherapy Collaborat Innovat Ctr, Chengdu 610041, Sichuan, Peoples R China;[4]East China Univ Sci & Technol, Shanghai Key Lab New Drug Design, Sch Pharm, 130 Meilong Rd, Shanghai 200237, Peoples R China;[5]East China Univ Sci & Technol, Shanghai, Peoples R China

年份：2017

卷号：18

期号：2

起止页码：333

外文期刊名：BRIEFINGS IN BIOINFORMATICS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000397060200016)】；

基金：This work was supported by the National Natural Science Foundation of China (grant no. 81373328, 81373329 and 81573020), the 863 Project (grant no. 2012AA020308), the Fundamental Research Funds for the Central Universities (grant no. WY1113007) and the 111 Project (grant no. B07023).

语种：英文

外文关键词：drug-target interaction; drug repositioning; network-based inference; chemoinformatics; failed drug; chemical substructure

摘要：Computational prediction of drug-target interactions (DTIs) and drug repositioning provides a low-cost and high-efficiency approach for drug discovery and development. The traditional social network-derived methods based on the naive DTI topology information cannot predict potential targets for new chemical entities or failed drugs in clinical trials. There are currently millions of commercially available molecules with biologically relevant representations in chemical databases. It is urgent to develop novel computational approaches to predict targets for new chemical entities and failed drugs on a large scale. In this study, we developed a useful tool, namely substructure-drug-target network-based inference (SDTNBI), to prioritize potential targets for old drugs, failed drugs and new chemical entities. SDTNBI incorporates network and chemoinformatics to bridge the gap between new chemical entities and known DTI network. High performance was yielded in 10-fold and leave-one-out cross validations using four benchmark data sets, covering G protein-coupled receptors, kinases, ion channels and nuclear receptors. Furthermore, the highest areas under the receiver operating characteristic curve were 0.797 and 0.863 for two external validation sets, respectively. Finally, we identified thousands of new potential DTIs via implementing SDTNBI on a global network. As a proof-of-principle, we showcased the use of SDTNBI to identify novel anticancer indications for nonsteroidal anti-inflammatory drugs by inhibiting AKR1C3, CA9 or CA12. In summary, SDTNBI is a powerful network-based approach that predicts potential targets for new chemical entities on a large scale and will provide a new tool for DTI prediction and drug repositioning. The program and predicted DTIs are available on request.