Division of System Cohort, Multi-scale Research Center for Medical Science, Medical Institute of Bioregulation, Kyushu University , 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan.
Graduate School of Bioscience and Biotechnology, Tokyo Institute of Technology , 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8550, Japan.
J Chem Inf Model. 2015 Dec 28;55(12):2717-30. doi: 10.1021/acs.jcim.5b00330. Epub 2015 Dec 1.
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.
药物重定位，或已知药物的新适应症的鉴定，是药物发现的一种有用策略。在这项研究中，我们开发了新的计算方法，利用大规模的化学-蛋白质相互作用组数据，对系统性药物重定位进行潜在药物靶点和新药物适应症的预测。我们通过充分利用数百万种化合物-蛋白质相互作用，基于化学结构相似性和表型效应相似性来探索药物的靶标空间（包括主要靶标和脱靶标）。基于药物的靶标特征，我们构建了统计模型，以预测具有各种分子特征的广泛疾病的新药物适应症。与以前的方法相比，该方法在可解释性、适用性和准确性方面都具有优势。最后，我们对 8270 种药物和 1401 种疾病的药物-靶标-疾病关联网络进行了全面预测，并展示了新预测的药物靶标和药物适应症的生物学意义上的例子。该预测模型有助于理解预测药物适应症的机制。
