Many embedding approaches of drugs have been proposed for the downstream task of drug-drug interaction (DDI) prediction in a DDI-derived network where drugs are considered nodes, and interactions are represented as edges. One of the most popular approaches is learning the representation of a drug from the DDI network by aggregating the features or information of its neighboring drugs. However, existing methods do not consider the specific type of the relation between the drugs, leading to an incomplete embedding learning process. Given that different relations between drugs may have different effects on drug embedding, the combination of multirelational embedding and relation-aware network structure embedding of drugs can be helpful to improve the prediction of DDIs. Therefore, in this paper, a relation-aware network embedding model for the prediction of drug-drug interactions (RANEDDI) is proposed. RANEDDI not only considers the multirelational information between drugs but also integrates the relation-aware network structure information in the topology of a multirelational DDI network to obtain the drug embedding. Under evaluation metrics such as AUC, AUPR and F1, the experimental results show that RANEDDI is superior to several state-of-the-art methods and can be used in the prediction of binary and multirelational DDIs. We also perform ablation studies that demonstrate that RANEDDI is effective and that it is robust in the task of binary DDI prediction, even in the case of a scarcity of labeled DDIs. The source code is freely available at https://github.com/DongWenMin/RANEDDI.

已经提出了许多药物嵌入方法用于 DDI 衍生网络中药物相互作用 (DDI) 预测的下游任务，其中药物被视为节点，相互作用表示为边。最流行的方法之一是通过聚合相邻药物的特征或信息，从 DDI 网络中学习药物的表示。然而，现有方法没有考虑药物之间关系的具体类型，导致嵌入学习过程不完整。鉴于药物之间的不同关系可能对药物嵌入产生不同的影响，多关系嵌入和关系感知网络结构的结合药物嵌入有助于提高对 DDI 的预测。因此，在本文中，提出了一种用于预测药物相互作用的关系感知网络嵌入模型（RANEDDI）。RANEDDI 不仅考虑了药物之间的多关系信息，还将关系感知的网络结构信息集成到多关系 DDI 网络的拓扑结构中，以获得药物嵌入。在AUC、AUPR和F1等评价指标下，实验结果表明RANEDDI优于几种最先进的方法，可用于二元和多关系DDI的预测。我们还进行了消融研究，证明 RANEDDI 是有效的，并且即使在标记 DDI 稀缺的情况下，它在二元 DDI 预测任务中也是稳健的。