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Computationally Probing Drug-Protein Interactions Via Support Vector Machine

文献类型：期刊论文

作者Wang, Yong-Cui1; Yang, Zhi-Xia2; Wang, Yong3; Deng, Nai-Yang1
刊名LETTERS IN DRUG DESIGN & DISCOVERY
出版日期2010-06-01
卷号7期号:5页码:370-378
关键词Drug-target interaction Chemical structure Protein sequence Imbalance problem Support vector machine
ISSN号1570-1808
英文摘要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.
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
语种英语
WOS记录号WOS:000277137100010
出版者BENTHAM SCIENCE PUBL LTD
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/10594]  
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.

入库方式： OAI收割

来源：数学与系统科学研究院

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