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.

Objective: Adverse event reports (AERs) submitted to the US Food and Drug Administration (FDA) were reviewed to confirm the platinum agent-associated mild, severe, and lethal hypersensitivity reactions.Methods: Authorized pharmacovigilance tools were used for quantitative signal detection, including the proportional reporting ratio, the reporting odds ratio, the information component given by a Bayesian confidence propagation neural network, and the empirical Bayes geometric mean. Excess2, given by the multi-item gamma Poisson Shrinker algorithm, was used to evaluate the effects of dexamethasone and diphenhydramine on oxaliplatin-induced hypersensitivity reactions.Results: Based on 1,644,220 AERs from 2004 to 2009, carboplatin and oxaliplatin proved to cause mild, severe, and lethal hypersensitivity reactions, whereas cisplatin did not. Dexamethasone affected oxaliplatin-induced mild hypersensitivity reactions, but had lesser effects on severe and lethal reactions. The effects of diphenhydramine were not confirmed.Conclusion: The FDA's adverse event reporting system, AERS, with optimized data mining tools is useful to authorize potential associations between platinum agents and hypersensitivity reactions.

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