The proposed ZSCAN-DDIE framework enhances prediction accuracy for unseen DDIE categories but also provides biologically meaningful insights into molecular interaction mechanisms, offering a robust and clinically relevant solution for pharmacovigilance and drug safety assessment.
Abstract
Unexpected drug–drug interaction events (DDIEs) pose substantial clinical risks, yet many remain unannotated due to data scarcity and the rapid emergence of novel drug combinations. Conventional deep learning approaches struggle to generalize to these unseen interaction types and often lack interpretability under severe class imbalance. To address these challenges, we propose ZSCAN-DDIE, an interpretable zero-shot learning framework for DDIE prediction. The model integrates a biomedical pre-trained language model with an attention-based graph convolutional network (AGCN) to encode DDIE textual semantics and drug molecular structures, respectively. A cross-attention network (CAN) is introduced to align molecular substructures with pharmacological semantic components, enabling fine-grained cross-modal reasoning and improving interpretability at the substructure level. To mitigate modality bias and long-tailed distribution effects, we design a bimodal dynamic alignment (BDA) loss that combines hyperspherical embedding regularization with a stage-adaptive loss-switching mechanism. Experimental results under both conventional and generalized zero-shot settings demonstrate that ZSCAN-DDIE consistently outperforms state-of-the-art baselines across multiple evaluation metrics. The proposed framework not only enhances prediction accuracy for unseen DDIE categories but also provides biologically meaningful insights into molecular interaction mechanisms, offering a robust and clinically relevant solution for pharmacovigilance and drug safety assessment
The source code and data are available at https://github.com/GSX-0429/ZSCAN-DDIE.
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