Computationally Probing Drug-Protein Interactions Via Support Vector Machine

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	Computationally Probing Drug-Protein Interactions Via Support Vector Machine
	Wang, Yong-Cui 1; Yang, Zhi-Xia 2; Wang, Yong3 ; Deng, Nai-Yang 1
	2010-06-01
发表期刊	LETTERS IN DRUG DESIGN & DISCOVERY
ISSN	1570-1808
卷号	7 期号:5 页码:370-378
摘要	The past decades witnessed extensive efforts to study the relationships among small molecules (drugs, metabolites, or ligands) and proteins due to the scale and complexity of their physical and genetic interactions. Particularly, computationally predicting the drug-protein interactions is fundamentally important in speeding up the process of developing novel therapeutic agents. Here, we present a supervised learning method, support vector machine (SVM), to predict drug-protein interactions by introducing two machine learning ideas. Firstly, the chemical structure similarity among drugs and the genomic sequence similarity among proteins are intuitively encoded as a feature vector to represent a given drug-protein pair. Secondly, we design an automatic procedure to select a gold-standard negative dataset to deal with the training data imbalance issue, i.e., gold-standard positive data is scarce relative to large scale unlabeled data. Our SVM based predictor is validated on four classes of drug target proteins, including enzymes, ion channels, G-protein couple receptors, and nuclear receptors. We find that our method improves the existing methods regarding to true positive rate upon given false positive rate. The functional annotation analysis and database search indicate that our new predictions are worthy of future experimental validation. In addition, follow-up analysis suggests that our method can partly capture the topological features in the drug-protein interaction network. In conclusion, our new method can efficiently identify the potential drug-protein bindings and will promote the further research in drug discovery.
关键词	Drug-target interaction Chemical structure Protein sequence Imbalance problem Support vector machine
语种	英语
资助项目	Key Project of the National Natural Science Foundation of China[10631070] ; Key Project of the National Natural Science Foundation of China[10801131] ; Key Project of the National Natural Science Foundation of China[10801112] ; Key Project of the National Natural Science Foundation of China[10971223] ; Ph.D Graduate Start Research Foundation of Xinjiang University[BS080101]
WOS研究方向	Pharmacology & Pharmacy
WOS类目	Chemistry, Medicinal
WOS记录号	WOS:000277137100010
出版者	BENTHAM SCIENCE PUBL LTD
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/10595
专题	应用数学研究所
通讯作者	Deng, Nai-Yang
作者单位	1.China Agr Univ, Coll Sci, Beijing 100083, Peoples R China 2.Xinjiang Univ, Coll Math & Syst Sci, Urumuchi 830046, Peoples R China 3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Wang, Yong-Cui,Yang, Zhi-Xia,Wang, Yong,et al. Computationally Probing Drug-Protein Interactions Via Support Vector Machine[J]. LETTERS IN DRUG DESIGN & DISCOVERY,2010,7(5):370-378.
APA	Wang, Yong-Cui,Yang, Zhi-Xia,Wang, Yong,&Deng, Nai-Yang.(2010).Computationally Probing Drug-Protein Interactions Via Support Vector Machine.LETTERS IN DRUG DESIGN & DISCOVERY,7(5),370-378.
MLA	Wang, Yong-Cui,et al."Computationally Probing Drug-Protein Interactions Via Support Vector Machine".LETTERS IN DRUG DESIGN & DISCOVERY 7.5(2010):370-378.