Identifying Side Effects of Adverse Drug Reactions via Drug-Drug Interactions with Advanced Neural Network Techniques

Abstract: Adverse Drug Reactions (ADRs) due to drug-drug interactions pose a significant public health issue, impacting mortality, morbidity, and healthcare costs. The increasing complexity of therapeutics and aging populations intensify these challenges. Currently, no standard method exists to detect such ADRs before drugs reach the market, as rare interactions often emerge only after patient reports. Clinical trials struggle to capture these rare effects. Thus, a reliable technique to predict ADRs prior to drug release is urgently needed. We propose an effective framework that models drug-drug interactions using Graph Neural Networks (GNNs) and self-supervised learning. By representing drugs as molecular graphs, our approach leverages their spatial and physical properties to enhance predictive capabilities, offering a promising solution to mitigate ADR risks early in the drug development process.