RCAN-DDI: Relation-aware cross adversarial network for drug-drug interaction prediction

Yuanyuan Zhang; Xiaoyu Xu; Baoming Feng; Haoyu Zheng; Ci'ao Zhang; Wentao Xu; Zengqian Deng

doi:10.1016/j.jpha.2024.101159

Volume 15 Issue 9

Sep. 2025

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Yuanyuan Zhang, Xiaoyu Xu, Baoming Feng, Haoyu Zheng, Ci'ao Zhang, Wentao Xu, Zengqian Deng. RCAN-DDI: Relation-aware cross adversarial network for drug-drug interaction prediction[J]. Journal of Pharmaceutical Analysis, 2025, 15(9): 101159. doi: 10.1016/j.jpha.2024.101159

Citation:

Yuanyuan Zhang, Xiaoyu Xu, Baoming Feng, Haoyu Zheng, Ci'ao Zhang, Wentao Xu, Zengqian Deng. RCAN-DDI: Relation-aware cross adversarial network for drug-drug interaction prediction[J]. Journal of Pharmaceutical Analysis, 2025, 15(9): 101159. doi: 10.1016/j.jpha.2024.101159

Citation:

Yuanyuan Zhang, Xiaoyu Xu, Baoming Feng, Haoyu Zheng, Ci'ao Zhang, Wentao Xu, Zengqian Deng. RCAN-DDI: Relation-aware cross adversarial network for drug-drug interaction prediction[J]. Journal of Pharmaceutical Analysis, 2025, 15(9): 101159. doi: 10.1016/j.jpha.2024.101159

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RCAN-DDI: Relation-aware cross adversarial network for drug-drug interaction prediction

doi: 10.1016/j.jpha.2024.101159

School of Information and Control Engineering, Qingdao University of Technology, Qingdao, Shandong, 266520, China

Funds:

This work was partially supported by the Natural Science Foundation of Shandong Province (Grant No.: ZR2023MF053) and the National Natural Science Foundation of China (Grant No.: 61902430).

Received Date: Jun. 26, 2024
Accepted Date: Dec. 04, 2024
Rev Recd Date: Nov. 21, 2024
Publish Date: Sep. 30, 2025

Abstract

Abstract

Drug-drug interaction (DDI) refers to the interaction between two or more drugs in the body, altering their efficacy or pharmacokinetics. Fully considering and accurately predicting DDI has become an indispensable part of ensuring safe medication for patients. In recent years, many deep learning-based methods have been proposed to predict DDI. However, most existing computational models tend to oversimplify the fusion of drug structural and topological information, often relying on methods such as splicing or weighted summation, which fail to adequately capture the potential complementarity between structural and topological features. This loss of information may lead to models that do not fully leverage these features, thus limiting their performance in DDI prediction. To address these challenges, we propose a relation-aware cross adversarial network for predicting DDI, named RCAN-DDI, which combines a relationship-aware structure feature learning module and a topological feature learning module based on DDI networks to capture multimodal features of drugs. To explore the correlations and complementarities among different information sources, the cross-adversarial network is introduced to fully integrate features from various modalities, enhancing the predictive performance of the model. The experimental results demonstrate that the RCAN-DDI method outperforms other methods. Even in cases of labelled DDI scarcity, the method exhibits good robustness in the DDI prediction task. Furthermore, the effectiveness of the cross-adversarial module is validated through ablation experiments, demonstrating its superiority in learning multimodal complementary information.
- Drug drug interaction,
- Structural features,
- DDI network,
- Cross adversarial network,
- Relation-aware

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References(50)

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