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      Target-Based Drug Repositioning Using Large-Scale Chemical-Protein Interactome Data.

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          Abstract

          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.

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          Author and article information

          Journal
          J Chem Inf Model
          Journal of chemical information and modeling
          American Chemical Society (ACS)
          1549-960X
          1549-9596
          Dec 28 2015
          : 55
          : 12
          Affiliations
          [1 ] 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.
          [2 ] Graduate School of Bioscience and Biotechnology, Tokyo Institute of Technology , 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8550, Japan.
          [3 ] Institute for Advanced Study, Kyushu University , 6-10-1, Hakozaki, Higashi-ku, Fukuoka 812-8581, Japan.
          Article
          10.1021/acs.jcim.5b00330
          26580494
          ad73138f-cdec-4b11-b7d7-4883d51e139a
          History

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