Predicting rare drug-drug interaction events with dual-granular structure-adaptive and pair variational representation

Zhonghao Ren, Xiangxiang Zeng, Yizhen Lao, Zhuhong You, Yifan Shang, Quan Zou () and Chen Lin ()
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Zhonghao Ren: Hunan University
Xiangxiang Zeng: Hunan University
Yizhen Lao: Hunan University
Zhuhong You: Northwestern Polytechnical University
Yifan Shang: Hunan University
Quan Zou: University of Electronic Science and Technology of China
Chen Lin: Xiamen University

Nature Communications, 2025, vol. 16, issue 1, 1-18

Abstract: Abstract Adverse drug-drug interaction events (DDIEs) pose serious risks to patient safety, yet rare but severe interactions remain challenging to identify due to limited clinical data. Existing computational methods rely heavily on abundant samples, failing to identify rare DDIEs. Here we introduce RareDDIE, a metric-based meta-learning model that employs a dual-granular structure-driven pair variational representation to enhance rare DDIE prediction. To further address the challenge of zero-shot DDIE identification, we develop the Biological Semantic Transferring (BST) module, integrating large-scale sentence embeddings to form the ZetaDDIE variant. Our model outperforms existing methods in few-sample and zero-sample settings. Furthermore, we verify that knowledge transfer from DDIE can improve drug synergy predictions, surpassing existing models. Case studies on antiplatelet activity reduction and non-small cell lung cancer drug synergy further illustrate the practical value of RareDDIE. By analyzing the meta-knowledge construction process, we provide interpretability into the model’s decision-making. This work establishes an effective computational framework for rare DDIE prediction, leveraging meta-learning and knowledge transfer to overcome key challenges in data-limited scenarios.

Date: 2025
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DOI: 10.1038/s41467-025-59431-9

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