Analysis of multiple compound–protein interactions reveals novel bioactive molecules

Molecular Systems Biology

Hiroaki Yabuuchi³  Satoshi Niijima³  Hiromu Takematsu²  Tomomi Ida³  Takatsugu Hirokawa⁴  Takafumi Hara¹  Teppei Ogawa³  Yohsuke Minowa³  Gozoh Tsujimoto¹ 

[1] Department of Genomic Drug Discovery Science, Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto, Japan;Laboratory of Membrane Biochemistry and Biophysics, Graduate School of Biostudies, Kyoto University, Kyoto, Japan;Department of Systems Biosciences for Drug Discovery, Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto, Japan;Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan

关键词: chemical genomics;    data mining;    drug discovery;    ligand screening;    systems chemical biology;   

DOI  :  10.1038/msb.2011.5

来源: Wiley

【 摘 要 】

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 com

【 授权许可】

CC BY-NC-SA   
Copyright © 2011 EMBO and Macmillan Publishers Limited

Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.