Multi-view Graph Contrastive Representation Learning for Drug-Drug Interaction Prediction [article]

Yingheng Wang, Yaosen Min, Xin Chen, Ji Wu
<span title="2020-12-29">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
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
more &raquo; ... 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="">arXiv:2010.11711v2</a> <a target="_blank" rel="external noopener" href="">fatcat:ryzrp7rosnblhc3bpcgnrmvxp4</a> </span>
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