DrugDAGT: a dual-attention graph transformer with contrastive learning improves drug-drug interaction prediction.
Yaojia Chen, Jiacheng Wang, Quan Zou, Mengting Niu, Yijie Ding, Jiangning Song, Yansu Wang
Author Information
Yaojia Chen: Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.
Jiacheng Wang: Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China.
Quan Zou: Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.
Mengting Niu: School of Electronic and Communication Engineering, Shenzhen Polytechnic University, Shenzhen, China.
Yijie Ding: Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China.
Jiangning Song: Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC, 3800, Australia. Jiangning.Song@monash.edu.
Yansu Wang: Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China. wangyansu@uestc.edu.cn.
BACKGROUND: Drug-drug interactions (DDIs) can result in unexpected pharmacological outcomes, including adverse drug events, which are crucial for drug discovery. Graph neural networks have substantially advanced our ability to model molecular representations; however, the precise identification of key local structures and the capture of long-distance structural correlations for better DDI prediction and interpretation remain significant challenges. RESULTS: Here, we present DrugDAGT, a dual-attention graph transformer framework with contrastive learning for predicting multiple DDI types. The dual-attention graph transformer incorporates attention mechanisms at both the bond and atomic levels, thereby enabling the integration of short and long-range dependencies within drug molecules to pinpoint key local structures essential for DDI discovery. Moreover, DrugDAGT further implements graph contrastive learning to maximize the similarity of representations across different views for better discrimination of molecular structures. Experiments in both warm-start and cold-start scenarios demonstrate that DrugDAGT outperforms state-of-the-art baseline models, achieving superior overall performance. Furthermore, visualization of the learned representations of drug pairs and the attention map provides interpretable insights instead of black-box results. CONCLUSIONS: DrugDAGT provides an effective tool for accurately predicting multiple DDI types by identifying key local chemical structures, offering valuable insights for prescribing medications, and guiding drug development. All data and code of our DrugDAGT can be found at https://github.com/codejiajia/DrugDAGT .
