  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.
