Analysis of multiple compound–protein interactions reveals novel bioactive molecules |
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Authors: | Hiromu Takematsu Tomomi Ida Takatsugu Hirokawa Takafumi Hara Teppei Ogawa Yohsuke Minowa Gozoh Tsujimoto Yasushi Okuno |
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Affiliation: | 1. Laboratory of Membrane Biochemistry and Biophysics, Graduate School of Biostudies, Kyoto University, , Kyoto, Japan;2. Department of Systems Biosciences for Drug Discovery, Graduate School of Pharmaceutical Sciences, Kyoto University, , Kyoto, Japan;3. Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, , Tokyo, Japan;4. Department of Genomic Drug Discovery Science, Graduate School of Pharmaceutical Sciences, Kyoto University, , Kyoto, Japan |
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Abstract: | The discovery of novel bioactive molecules advances our systems‐level understanding of biological processes and is crucial for innovation in drug development. For this purpose, the emerging field of chemical genomics is currently focused on accumulating large assay data sets describing compound–protein interactions (CPIs). Although new target proteins for known drugs have recently been identified through mining of CPI databases, using these resources to identify novel ligands remains unexplored. Herein, we demonstrate that machine learning of multiple CPIs can not only assess drug polypharmacology but can also efficiently identify novel bioactive scaffold‐hopping compounds. Through a machine‐learning technique that uses multiple CPIs, we have successfully identified novel lead compounds for two pharmaceutically important protein families, G‐protein‐coupled receptors and protein kinases. These novel compounds were not identified by existing computational ligand‐screening methods in comparative studies. The results of this study indicate that data derived from chemical genomics can be highly useful for exploring chemical space, and this systems biology perspective could accelerate drug discovery processes. |
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Keywords: | chemical genomics data mining drug discovery ligand screening systems chemical biology |
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