ProAlign-DDI: A Zero-Shot Drug–Drug Interaction Event Prediction Framework with Prototype Alignment

Abstract The accurate identification of drug–drug interaction (DDI) events (DDIEs) is essential for ensuring medication safety and preventing adverse effects. However, novel drug development constantly generates new DDIE classes that suffer from a severe scarcity of labels, making zero-shot learning an essential solution for emerging cases. Unfortunately, the long-tailed distribution of existing DDI datasets causes overfitting to head classes and poor generalization to tail classes, thereby severely impairing zero-shot performance. To address this limitation, we propose a novel framework, ProAlign-DDI, which integrates a Substructure-Guided Semantic Adaptive Aggregation Module (SAAM) with a Prototype Alignment Module (PAM). In this framework, SAAM adaptively captures task-relevant features to produce high-quality aggregated representations. Subsequently, PAM employs class-level semantic prototypes to regularize these representations. This alignment strategy not only optimizes the global feature space but also alleviates the bias inherent in long-tailed DDI datasets. Comprehensive experiments show that ProAlign-DDI achieves competitive performance in conventional zero-shot learning and more balanced performance in generalized zero-shot learning, confirming its effectiveness in inferring unseen DDIE classes.