Prediction of adverse drug reaction-related proteins using large-scale biological network embedding

Abstract

Adverse drug reactions (ADRs) are a major issue in drug development and clinical pharmacology. As most ADRs are caused by unintended activity at off-targets of drugs, the identification of drug targets responsible for ADRs becomes a key process for resolving ADRs. Recently, with the increase in the number of ADR-related data sources, several computational methodologies have been proposed to analyze ADR-protein relations. However, the identification of ADR-related proteins on a large scale with high reliability remains an important challenge. In this study, we suggest a computational approach, which combines a novel concept called single-target compound with a large-scale bio-network embedding technique to enable large-scale prediction of ADR-related proteins for any proteins in the protein-protein interaction network. Suggesting approach provides more reliable predictions for ADR-related proteins, compared to a previously proposed method. Furthermore, two case studies show that most predictive proteins related to ADRs are supported by literature evidence. Overall, this study can provide reliable insights into the relationship between ADRs and proteomes to understand the mechanism of ADRs leading to their prevention.