The authors use machine learning of compound-protein interactions to explore drug polypharmacology and to efficiently identify bioactive ligands, including novel scaffold-hopping compounds for two pharmaceutically important protein families: G-protein coupled receptors and protein kinases.

G-protein coupled receptors (GPCRs) represent one of the most important families of drug targets in pharmaceutical development. GLIDA is a public GPCR-related Chemical Genomics database that is primarily focused on the integration of information between GPCRs and their ligands. It provides interaction data between GPCRs and their ligands, along with chemical information on the ligands, as well as biological information regarding GPCRs. These data are connected with each other in a relational database, allowing users in the field of Chemical Genomics research to easily retrieve such information from either biological or chemical starting points. GLIDA includes a variety of similarity search functions for the GPCRs and for their ligands. Thus, GLIDA can provide correlation maps linking the searched homologous GPCRs (or ligands) with their ligands (or GPCRs). By analyzing the correlation patterns between GPCRs and ligands, we can gain more detailed knowledge about their conserved molecular recognition patterns and improve drug design efforts by focusing on inferred candidates for GPCR-specific drugs. This article provides a summary of the GLIDA database and user facilities, and describes recent improvements to database design, data contents, ligand classification programs, similarity search options and graphical interfaces. GLIDA is publicly available at http://pharminfo.pharm.kyoto-u.ac.jp/services/glida/. We hope that it will prove very useful for Chemical Genomics research and GPCR-related drug discovery.

The development of selective and multitargeted kinase inhibitors has received much attention, because cross-reactivity with unintended targets may cause toxic side effects, while it can also give rise to efficacious multitargeted drugs. Here we describe a deconvolution approach to dissecting kinase profiling data in order to gain knowledge about cross-reactivity of inhibitors from large-scale profiling data. This approach not only enables activity predictions of given compounds on a kinome-wide scale, but also allows to extract residue-fragment pairs that are associated with activity. We demonstrate its effectiveness using a large-scale public chemogenomics data set and also apply our proposed model to a recently published bioactivity data set. We further illustrate that the preference of given compounds for kinases of interest is better understood by residue-fragment pairs, which could provide both biological and chemical insights into cross-reactivity.