Detecting Side Effects of Adverse Drug Reactions through Drug-Drug Interactions

Adverse drug reactions (ADRs) from drug-drug interactions (DDIs) are a major public health and healthcare cost problem worldwide. Increasing complexity of treatments and ageing populations make managing ADRs more challenging. Standard methods for predicting these reactions do not exist as ADRs are often not detected until reported post-market by patients. Here we present a novel framework using Graph Neural Networks (GNNs) with self-supervised learning to model and predict ADRs from DDIs. By representing drugs as molecular graphs we capture spatial and chemical properties of drug interactions. This research contributes to pharmacovigilance by providing a robust framework to identify potential DDIs and support clinical decision-making