计算机科学
药物发现
推论
药物靶点
交互网络
计算生物学
相似性(几何)
化学相似性
鉴定(生物学)
药品
机器学习
人工智能
数据挖掘
化学空间
生物
生物信息学
结构相似性
遗传学
基因
图像(数学)
药理学
植物
作者
Yoshihiro Yamanishi,Michihiro Araki,Alex Gutteridge,Wataru Honda,Minoru Kanehisa
出处
期刊:Bioinformatics
[Oxford University Press]
日期:2008-06-27
卷期号:24 (13): i232-i240
被引量:995
标识
DOI:10.1093/bioinformatics/btn162
摘要
The identification of interactions between drugs and target proteins is a key area in genomic drug discovery. Therefore, there is a strong incentive to develop new methods capable of detecting these potential drug-target interactions efficiently.In this article, we characterize four classes of drug-target interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, and reveal significant correlations between drug structure similarity, target sequence similarity and the drug-target interaction network topology. We then develop new statistical methods to predict unknown drug-target interaction networks from chemical structure and genomic sequence information simultaneously on a large scale. The originality of the proposed method lies in the formalization of the drug-target interaction inference as a supervised learning problem for a bipartite graph, the lack of need for 3D structure information of the target proteins, and in the integration of chemical and genomic spaces into a unified space that we call 'pharmacological space'. In the results, we demonstrate the usefulness of our proposed method for the prediction of the four classes of drug-target interaction networks. Our comprehensively predicted drug-target interaction networks enable us to suggest many potential drug-target interactions and to increase research productivity toward genomic drug discovery.Softwares are available upon request.Datasets and all prediction results are available at http://web.kuicr.kyoto-u.ac.jp/supp/yoshi/drugtarget/.
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