Identification of Drug-Drug Interactions Using Chemical Interactions

doi:10.2174/1574893611666160618094219

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	Identification of Drug-Drug Interactions Using Chemical Interactions
	Chen, Lei 1; Chu, Chen 2; Zhang, Yu-Hang 3; Zheng, Mingyue4 ; Zhu, LiuCun 5; Kong, Xiangyin 3; Huang, Tao 3
刊名	CURRENT BIOINFORMATICS
	2017
卷号	12 期号:6 页码:526-534
关键词	Drug-drug interaction chemical interaction chemical structure similarity nearest neighbor algorithm majority voting imbalanced dataset
ISSN号	1574-8936
DOI	10.2174/1574893611666160618094219
文献子类	Article
英文摘要	Background: One drug can affect the activity of another when they are administered together, which can cause adverse drug reactions or sometimes improve therapeutic effects. Therefore, correct identification of drug-drug interactions (DDIs) can help medical workers use various drugs effectively, avoiding adverse effects and improving therapeutic effects. Methods: This study proposed a novel prediction model to identify DDIs. A new metric was constructed to evaluate the similarity of two pairs of drugs using chemical interaction information retrieved from STITCH. Validated DDIs retrieved from DrugBank were employed, from which we constructed all possible pairs of drugs that were deemed as negative samples. The whole dataset was divided into one training dataset and one test dataset. To address the imbalanced data, a complicated dataset compilation strategy was adopted to construct nine training datasets from the original training dataset, reducing the ratio of positive samples and negative samples. Nine predictors based on the nearest neighbor algorithm were built based on these training datasets. The proposed model integrated the above nine predictors by majority voting and its performance was evaluated on the test dataset. Results: The predicted results indicate that the method is quite effective for identification of DDIs. Finally, we also discussed the ability of the method for identifying novel DDIs by investigating the likelihood of some negative samples in the test dataset that were predicted as DDIs being novel DDIs. Conclusion: The proposed method has a good ability for identification of potential DDIs.
资助项目	National Natural Science Foundation of China[61202021] ; National Natural Science Foundation of China[61303099]
WOS关键词	MUSCARINIC M2 RECEPTORS ; IN-VITRO ; PHARMACOKINETIC INTERACTION ; OLANZAPINE TREATMENT ; PREDICTION ; CARBAMAZEPINE ; BUPIVACAINE ; INFORMATION ; PROTEINS ; RATS
WOS研究方向	Biochemistry & Molecular Biology ; Mathematical & Computational Biology
语种	英语
出版者	BENTHAM SCIENCE PUBL LTD
WOS记录号	WOS:000418706100006
内容类型	期刊论文
源URL	[http://119.78.100.183/handle/2S10ELR8/275689]
专题	药物发现与设计中心中科院受体结构与功能重点实验室新药研究国家重点实验室
通讯作者	Chen, Lei; Huang, Tao
作者单位	1.Shanghai Maritime Univ, Coll Informat Engn, Shanghai 201306, Peoples R China; 2.Chinese Acad Sci, Shanghai Inst Biol Sci, Inst Biochem & Cell Biol, Shanghai 200031, Peoples R China; 3.Chinese Acad Sci, Shanghai Inst Biol Sci, Inst Hlth Sci, Shanghai 200031, Peoples R China; 4.Chinese Acad Sci, Shanghai Inst Mat Med, Drug Discovery & Design Ctr, State Key Lab Drug Res, Shanghai 201203, Peoples R China; 5.Shanghai Univ, Coll Life Sci, Shanghai 200444, Peoples R China
推荐引用方式 GB/T 7714	Chen, Lei,Chu, Chen,Zhang, Yu-Hang,et al. Identification of Drug-Drug Interactions Using Chemical Interactions[J]. CURRENT BIOINFORMATICS,2017,12(6):526-534.
APA	Chen, Lei.,Chu, Chen.,Zhang, Yu-Hang.,Zheng, Mingyue.,Zhu, LiuCun.,...&Huang, Tao.(2017).Identification of Drug-Drug Interactions Using Chemical Interactions.CURRENT BIOINFORMATICS,12(6),526-534.
MLA	Chen, Lei,et al."Identification of Drug-Drug Interactions Using Chemical Interactions".CURRENT BIOINFORMATICS 12.6(2017):526-534.