Drug Drug Interaction

Drug-drug interaction (DDI) prediction aims to computationally identify the effects of combining medications, improving patient safety and drug development. Current research heavily utilizes graph neural networks (GNNs), including variations like graph attention networks and hypergraph neural networks, along with other deep learning architectures, to model complex relationships between drugs and their properties, often incorporating knowledge graphs and molecular substructures for enhanced accuracy and interpretability. These advanced computational methods offer significant potential for improving the prediction of both known and novel DDIs, ultimately leading to safer and more effective polypharmacy treatments.