Relation-aware graph structure embedding is promising for predicting multi-relational drug-drug interactions (DDIs). Typically, most existing methods begin by constructing a multi-relational DDI graph and then learning relation-aware graph structure embeddings (RaGSEs) of drugs from the DDI graph. Nevertheless, most existing approaches are usually limited in learning RaGSEs of new drugs, leading to serious over-fitting when the test DDIs involve such drugs. To alleviate this issue, we propose a novel DDI prediction method based on relation-aware graph structure embedding with co-contrastive learning, RaGSECo. The proposed RaGSECo constructs two heterogeneous drug graphs: a multi-relational DDI graph and a multi-attribute drug-drug similarity (DDS) graph. The two graphs are used respectively for learning and propagating the RaGSEs of drugs, aiming to ensure all drugs, including new ones, can possess effective RaGSEs. Additionally, we present a novel co-contrastive learning module to learn drug-pairs (DPs) representations. This mechanism learns DP representations from two distinct views (interaction and similarity views) and encourages these views to supervise each other collaboratively to obtain more discriminative DP representations. We evaluate the effectiveness of our RaGSECo on three different tasks using two real datasets. The experimental results demonstrate that RaGSECo outperforms existing state-of-the-art prediction methods.

翻译：关系感知的图结构嵌入在多关系药物相互作用（DDI）预测中具有广阔前景。通常，现有方法首先构建多关系DDI图，然后从该图中学习药物的关系感知图结构嵌入（RaGSEs）。然而，大多数现有方法在学习新药物的RaGSEs时通常存在局限性，当测试DDI涉及此类药物时会导致严重的过拟合。为解决该问题，我们提出了一种基于关系感知图结构嵌入与协同对比学习的新型DDI预测方法RaGSECo。所提出的RaGSECo构建了两个异质药物图：一个多关系DDI图和一个多属性药物-药物相似性（DDS）图。这两个图分别用于学习和传播药物的RaGSEs，旨在确保所有药物（包括新药）都能获得有效的RaGSEs。此外，我们提出了一个新颖的协同对比学习模块来学习药物对（DPs）表示。该机制从两种不同视角（相互作用视角和相似性视角）学习DP表示，并鼓励这些视角相互协同监督，以获得更具判别性的DP表示。我们在两个真实数据集上通过三种不同任务评估了RaGSECo的有效性。实验结果表明，RaGSECo优于现有最先进的预测方法。