title: Analysis of multiple compound-protein interactions reveals novel bioactive molecules.
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.
authors.affiliation: Department of Systems Biosciences for Drug Discovery, Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto, Japan.
authors.forename: Hiroaki
authors.initials: H
authors.lastname: Yabuuchi
authors.forename: Satoshi
authors.initials: S
authors.lastname: Niijima
authors.forename: Hiromu
authors.initials: H
authors.lastname: Takematsu
authors.forename: Tomomi
authors.initials: T
authors.lastname: Ida
authors.forename: Takatsugu
authors.initials: T
authors.lastname: Hirokawa
authors.forename: Takafumi
authors.initials: T
authors.lastname: Hara
authors.forename: Teppei
authors.initials: T
authors.lastname: Ogawa
authors.forename: Yohsuke
authors.initials: Y
authors.lastname: Minowa
authors.forename: Gozoh
authors.initials: G
authors.lastname: Tsujimoto
authors.forename: Yasushi
authors.initials: Y
authors.lastname: Okuno
mesh_terms: D001185:Artificial Intelligence; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D004304:Dosage Forms; D055808:Drug Discovery / Q000379:methods*; D023281:Genomics / Q000379:methods*; D006801:Humans; D008024:Ligands; D011494:Protein Kinases / Q000032:analysis* / Q000737:chemistry; D018343:Receptors, Adrenergic, beta-2 / Q000737:chemistry / Q000378:metabolism; D043562:Receptors, G-Protein-Coupled / Q000032:analysis* / Q000737:chemistry; D049490:Systems Biology
chemical_list: C553443:ADRB2 protein, human; D004304:Dosage Forms; D008024:Ligands; D018343:Receptors, Adrenergic, beta-2; D043562:Receptors, G-Protein-Coupled; D011494:Protein Kinases