Prediction of drug-target interaction networks from the integration of chemical and genomic spaces.
Prediction of drug-target interaction networks from the integration of chemical and genomic spaces.
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DOI：
10.1093/bioinformatics/btn162
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发表时间：
2008-07-01
期刊：
影响因子：
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通讯作者：
Kanehisa M
中科院分区：
文献类型：
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作者：
Yamanishi Y;Araki M;Gutteridge A;Honda W;Kanehisa M
目的：药物与靶蛋白相互作用的鉴定是基因组药物发现的关键领域。因此，开发能够有效检测这些潜在药物-靶标相互作用的新方法具有强烈的动机。结果如下：在这篇文章中，我们描述了四类药物靶点相互作用网络在人类涉及酶，离子通道，G-蛋白偶联受体（GPCR）和核受体，并揭示药物结构相似性，靶序列相似性和药物靶点相互作用网络拓扑结构之间的显着相关性。然后，我们开发了新的统计方法来预测未知的药物靶点相互作用网络的化学结构和基因组序列信息，同时在大规模上。该方法的独创性在于将药物-靶点相互作用推理形式化为二分图的监督学习问题，不需要靶蛋白的3D结构信息，并且将化学和基因组空间整合到一个统一的空间中，我们称之为“药理学空间”。在结果中，我们证明了我们提出的方法的有用性的四类药物-靶相互作用网络的预测。我们全面预测的药物-靶标相互作用网络使我们能够提出许多潜在的药物-靶标相互作用，并提高基因组药物发现的研究生产力。可用性：软件可根据要求提供。联系人：Yoshihiro. ensmp.fr补充信息：数据集和所有预测结果可在http://web.kuicr.kyoto-u.ac.jp/supp/yoshi/drugtarget/上获得。
Motivation: 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. Results: 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. Availability: Softwares are available upon request. Contact: Yoshihiro.Yamanishi@ensmp.fr Supplementary information: Datasets and all prediction results are available at http://web.kuicr.kyoto-u.ac.jp/supp/yoshi/drugtarget/.