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

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.