H2D: Hierarchical Heterogeneous Graph Learning Framework for Drug-Drug Interaction Prediction

Accurately predicting Drug-Drug Interactions (DDIs) is critical to designing effective drug combination therapies. Recently, Artificial Intelligence (AI)-powered DDI prediction approaches have emerged as a new paradigm. However, most existing methods oversimplify the complex hierarchical structure within molecules and overlook the multi-source heterogeneous information external to molecules, limiting their modeling and predictive capabilities. To address this, we propose a <u>H</u> ierarchical <u>H</u> eterogeneous graph learning framework for <u>D</u> DI prediction, namely H2D. H2D employs an internal-to-external, local-to-global hierarchical perspective, exploiting intra-molecular multi-granularity structures and inter-molecular biomedical interactions to mutually enhance across hierarchical levels. Extensive experimental results demonstrate H2D's effectiveness on three real-world DDI prediction tasks (binary-class, multi-class, and multi-label). In sum, H2D achieves state-of-the-art performance in DDI prediction by leveraging the multi-scale graph structures, opening up new avenues in AI-powered DDI prediction.