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In silico prediction of drug-target interaction networks based on drug chemical structure and protein sequences
Li, ZW (Li, Zhengwei); Han, PY (Han, Pengyong); You, ZH (You, Zhu-Hong); Li, X (Li, Xiao); Zhang, YS (Zhang, Yusen); Yu, HQ (Yu, Haiquan); Nie, R (Nie, Ru); Chen, X (Chen, Xing); You, ZH; Li, X
2017
发表期刊SCIENTIFIC REPORTS
卷号7期号:9
摘要

Analysis of drug-target interactions (DTIs) is of great importance in developing new drug candidates for known protein targets or discovering new targets for old drugs. However, the experimental approaches for identifying DTIs are expensive, laborious and challenging. In this study, we report a novel computational method for predicting DTIs using the highly discriminative information of drug-target interactions and our newly developed discriminative vector machine (DVM) classifier. More specifically, each target protein sequence is transformed as the position-specific scoring matrix (PSSM), in which the evolutionary information is retained; then the local binary pattern (LBP) operator is used to calculate the LBP histogram descriptor. For a drug molecule, a novel fingerprint representation is utilized to describe its chemical structure information representing existence of certain functional groups or fragments. When applying the proposed method to the four datasets (Enzyme, GPCR, Ion Channel and Nuclear Receptor) for predicting DTIs, we obtained good average accuracies of 93.16%, 89.37%, 91.73% and 92.22%, respectively. Furthermore, we compared the performance of the proposed model with that of the state-of-the-art SVM model and other previous methods. The achieved results demonstrate that our method is effective and robust and can be taken as a useful tool for predicting DTIs.

DOI10.1038/s41598-017-10724-0
收录类别SCI
WOS记录号WOS:000410064000010
引用统计
被引频次:3[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.xjipc.cas.cn/handle/365002/5036
专题多语种信息技术研究室
通讯作者You, ZH; Li, X
作者单位1.China Univ Min & Technol, Sch Comp Sci & Technol, Xuzhou 221116, Peoples R China
2.Univ Calgary, Cumming Sch Med, Calgary, AB T2N 4N1, Canada
3.Inner Mongolia Univ, Key Lab Mammal Reprod Biol & Biotechnol, Minist Educ, Hohhot 010021, Peoples R China
4.Chinese Acad Sci, Xinjiang Tech Inst Phys & Chem, Urumqi 830011, Peoples R China
5.Shandong Univ Weihai, Sch Math & Stat, Weihai 264209, Peoples R China
6.China Univ Min & Technol, Sch Informat & Control Engn, Xuzhou 21116, Peoples R China
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GB/T 7714
Li, ZW ,Han, PY ,You, ZH ,et al. In silico prediction of drug-target interaction networks based on drug chemical structure and protein sequences[J]. SCIENTIFIC REPORTS,2017,7(9).
APA Li, ZW .,Han, PY .,You, ZH .,Li, X .,Zhang, YS .,...&Li, X.(2017).In silico prediction of drug-target interaction networks based on drug chemical structure and protein sequences.SCIENTIFIC REPORTS,7(9).
MLA Li, ZW ,et al."In silico prediction of drug-target interaction networks based on drug chemical structure and protein sequences".SCIENTIFIC REPORTS 7.9(2017).
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