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- Title
MSEDDI: Multi-Scale Embedding for Predicting Drug—Drug Interaction Events.
- Authors
Yu, Liyi; Xu, Zhaochun; Cheng, Meiling; Lin, Weizhong; Qiu, Wangren; Xiao, Xuan
- Abstract
A norm in modern medicine is to prescribe polypharmacy to treat disease. The core concern with the co-administration of drugs is that it may produce adverse drug—drug interaction (DDI), which can cause unexpected bodily injury. Therefore, it is essential to identify potential DDI. Most existing methods in silico only judge whether two drugs interact, ignoring the importance of interaction events to study the mechanism implied in combination drugs. In this work, we propose a deep learning framework named MSEDDI that comprehensively considers multi-scale embedding representations of the drug for predicting drug—drug interaction events. In MSEDDI, we design three-channel networks to process biomedical network-based knowledge graph embedding, SMILES sequence-based notation embedding, and molecular graph-based chemical structure embedding, respectively. Finally, we fuse three heterogeneous features from channel outputs through a self-attention mechanism and feed them to the linear layer predictor. In the experimental section, we evaluate the performance of all methods on two different prediction tasks on two datasets. The results show that MSEDDI outperforms other state-of-the-art baselines. Moreover, we also reveal the stable performance of our model in a broader sample set via case studies.
- Subjects
DRUG interactions; KNOWLEDGE graphs; DEEP learning; CHEMICAL structure; DRUGS
- Publication
International Journal of Molecular Sciences, 2023, Vol 24, Issue 5, p4500
- ISSN
1661-6596
- Publication type
Article
- DOI
10.3390/ijms24054500