Most existing methods for predicting drug-drug interactions (DDI) predominantly concentrate on capturing the explicit relationships among drugs, overlooking the valuable implicit correlations present between drug pairs (DPs), which leads to weak predictions. To address this issue, this paper introduces a hierarchical multi-relational graph representation learning (HMGRL) approach. Within the framework of HMGRL, we leverage a wealth of drug-related heterogeneous data sources to construct heterogeneous graphs, where nodes represent drugs and edges denote clear and various associations. The relational graph convolutional network (RGCN) is employed to capture diverse explicit relationships between drugs from these heterogeneous graphs. Additionally, a multi-view differentiable spectral clustering (MVDSC) module is developed to capture multiple valuable implicit correlations between DPs. Within the MVDSC, we utilize multiple DP features to construct graphs, where nodes represent DPs and edges denote different implicit correlations. Subsequently, multiple DP representations are generated through graph cutting, each emphasizing distinct implicit correlations. The graph-cutting strategy enables our HMGRL to identify strongly connected communities of graphs, thereby reducing the fusion of irrelevant features. By combining every representation view of a DP, we create high-level DP representations for predicting DDIs. Two genuine datasets spanning three distinct tasks are adopted to gauge the efficacy of our HMGRL. Experimental outcomes unequivocally indicate that HMGRL surpasses several leading-edge methods in performance.

翻译：现有的大多数药物相互作用（DDI）预测方法主要侧重于捕捉药物间的显式关系，忽视了药物对（DPs）之间存在的重要隐式相关性，导致预测能力较弱。为解决此问题，本文提出了一种层次化多关系图表示学习方法（HMGRL）。在HMGRL框架中，我们利用丰富的药物相关异质数据源构建异质图，其中节点表示药物，边表示清晰多样的关联。采用关系图卷积网络（RGCN）从这些异质图中捕捉药物间的多种显式关系。此外，开发了多视角可微分谱聚类（MVDSC）模块以捕捉DPs之间的多种有价值隐式相关性。在MVDSC中，我们利用多种DP特征构建图，其中节点表示DPs，边表示不同的隐式相关性。随后，通过图切割生成多种DP表示，每种表示侧重于不同的隐式相关性。图切割策略使我们的HMGRL能够识别图的强连通社区，从而减少无关特征的融合。通过组合DP的每个表示视角，我们创建用于预测DDI的高级DP表示。采用涵盖三个不同任务的两个真实数据集评估HMGRL的性能。实验结果明确表明，HMGRL在性能上超越了多种领先方法。