Potential Drug-Drug Interaction(DDI) occurring while treating complex or co-existing diseases with drug combinations may cause changes in drugs' pharmacological activity. Therefore, DDI prediction has been an important task in the medical healthy machine learning community. Graph-based learning methods have recently aroused widespread interest and are proved to be a priority for this task. However, these methods are often limited to exploiting the inter-view drug molecular structure and ignoring the drug's intra-view interaction relationship, vital to capturing the complex DDI patterns. 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 GCN to encode DDI relationships and a bond-aware attentive message propagating method to capture drug molecular structure information in the MIRACLE learning stage. 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.

本研究提出了一种新的机器学习方法，称为 MIRACLE，采用多视角图形对比表示学习的方法，结合GCNs和关注力机制传递网络，实现药物分子结构及其相互作用信息的预测，其中的无监督对比学习方法能够兼顾数据的多视角信息，实验结果表明， MIRACLE 比现有预测模型表现更优。

药物-药物相互作用预测的多视角图对比表示学习