Target-Based Drug Repositioning Using Large-Scale Chemical-Protein Interactome Data
Target-Based Drug Repositioning Using Large-Scale Chemical-Protein Interactome Data
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DOI：
10.1021/acs.jcim.5b00330
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发表时间：
2015-12-01
影响因子：
5.6
通讯作者：
Yamanishi, Yoshihiro
中科院分区：
文献类型：
--
作者：
Sawada, Ryusuke;Iwata, Hiroaki;Yamanishi, Yoshihiro
药物重新定位，或已知药物的新适应症的识别，是药物发现的有用策略。在这项研究中，我们开发了新的计算方法来预测潜在的药物靶点和新的药物适应症，以便使用大规模的化学-蛋白质相互作用组数据进行系统的药物重新定位。我们通过优化利用数百万种化合物与蛋白质的相互作用，基于化学结构相似性和表型效应相似性来探索药物的靶点空间(包括主要靶点和非靶点)。在药物靶向分布的基础上，我们构建了统计模型来预测具有不同分子特征的广泛疾病的新药适应症。该方法在可解释性、适用性和准确性方面均优于以往的方法。最后，我们对8270种药物和1401种疾病的药物-靶点-疾病关联网络进行了全面的预测，并展示了具有生物学意义的新预测的药物靶点和药物适应症的例子。该预测模型有助于理解预测药物适应症的机理。
Drug repositioning, or the identification of new indications for known drugs, is a useful strategy for drug discovery. In this study, we developed novel computational methods to predict potential drug targets and new drug indications for systematic drug repositioning using large-scale chemical-protein interactome data. We explored the target space of drugs (including primary targets and off-targets) based on chemical structure similarity and phenotypic effect similarity by making optimal use of millions of compound-protein interactions. On the basis of the target profiles of drugs, we constructed statistical models to predict new drug indications for a wide range of diseases with various molecular features. The proposed method outperformed previous methods in terms of interpretability, applicability, and accuracy. Finally, we conducted a comprehensive prediction of the drug-target-disease association network for 8270 drugs and 1401 diseases and showed biologically meaningful examples of newly predicted drug targets and drug indications. The predictive model is useful to understand the mechanisms of the predicted drug indications.