Multi-view Graph Contrastive Representation Learning for Drug-Drug Interaction Prediction

• URL: http://arxiv.org/abs/2010.11711v3
• Date: Sat, 10 Apr 2021 08:30:57 GMT
• Title: Multi-view Graph Contrastive Representation Learning for Drug-Drug Interaction Prediction
• Authors: Yingheng Wang, Yaosen Min, Xin Chen, Ji Wu
• Abstract summary: This study presents a new method, multi-view graph contrastive representation learning for drug-drug interaction prediction, MIRACLE for brevity. We use GCNs and bond-aware attentive message passing networks to encode DDI relationships and drug molecular graphs in the MIRACLE learning stage. Experiments on multiple real datasets show that MIRACLE outperforms the state-of-the-art DDI prediction models consistently.
• Score: 11.87950055946236
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Drug-drug interaction(DDI) prediction is an important task in the medical health machine learning community. This study presents a new method, multi-view graph contrastive representation learning for drug-drug interaction prediction, MIRACLE for brevity, to capture inter-view molecule structure and intra-view interactions between molecules simultaneously. MIRACLE treats a DDI network as a multi-view graph where each node in the interaction graph itself is a drug molecular graph instance. We use GCNs and bond-aware attentive message passing networks to encode DDI relationships and drug molecular graphs in the MIRACLE learning stage, respectively. Also, we propose a novel unsupervised contrastive learning component to balance and integrate the multi-view information. Comprehensive experiments on multiple real datasets show that MIRACLE outperforms the state-of-the-art DDI prediction models consistently.

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