GNN-DDI: A New Data Integration Framework for Predicting Drug-Drug Interaction Events Based on Graph Neural Networks

Abstract

The prevalence of multi_drug therapies has been increasing in recent years, particularly among the elderly who are suffering from several diseases. However, unexpected Drug_Drug interaction (DDI) can cause adverse reactions or critical toxicity, which puts patients in danger. As the need for multi_drug treatments increases, it's becoming increasingly necessary to discover DDIs. Nevertheless, the DDIs detection in an extensive number of drug pairs, both in-vitro and in-vivo, is costly and laborious, Therefore, DDI identification is one of the most concerns in drug-related researches. In this paper, we propose GNN-DDI, a deep learning-based method for predicting DDI-associated events in two stages. In the first stage, it collects drugs information from different sources then integrates them through the formation of an attributed heterogeneous network. In the second stage, predictions of the DDIs and their events are performed through a deep multi-model framework. Various evaluation results show that the proposed method can outperform state-of-the methods in the prediction of drug-drug interaction-associated events. The experimental results indicate that using different data sources and integrating them through an attributed heterogeneous network for producing the node's representations is efficient and effective.