Attention-based cross domain graph neural network for prediction of drug–drug interactions

• Published in: Briefings in bioinformatics 2023-07, Vol.24 (4)
• Main Authors: Yu, Hui, Li, KangKang, Dong, WenMin, Song, ShuangHong, Gao, Chen, Shi, JianYu
• Format: Article
• Language: English
• Subjects: Drug interaction; Drug Interactions; Graph neural networks; Heterogeneity; Humans; Neural networks; Neural Networks, Computer; Predictions
• ISSN: 1467-5463; 1477-4054
• DOI: 10.1093/bib/bbad155

Abstract Drug–drug interactions (DDI) may lead to adverse reactions in human body and accurate prediction of DDI can mitigate the medical risk. Currently, most of computer-aided DDI prediction methods construct models based on drug-associated features or DDI network, ignoring the potential information contained in drug-related biological entities such as targets and genes. Besides, existing DDI network-based models could not make effective predictions for drugs without any known DDI records. To address the above limitations, we propose an attention-based cross domain graph neural network (ACDGNN) for DDI prediction, which considers the drug-related different entities and propagate information through cross domain operation. Different from the existing methods, ACDGNN not only considers rich information contained in drug-related biomedical entities in biological heterogeneous network, but also adopts cross-domain transformation to eliminate heterogeneity between different types of entities. ACDGNN can be used in the prediction of DDIs in both transductive and inductive setting. By conducting experiments on real-world dataset, we compare the performance of ACDGNN with several state-of-the-art methods. The experimental results show that ACDGNN can effectively predict DDIs and outperform the comparison models.