Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions

Background: Graph neural networks improve computational prediction of polypharmacy side effects, but standard binary cross-entropy training allocates equal capacity to well-classified and difficult examples, potentially missing clinically significant interactions. We evaluated whether an asymmetric ...