A Novel Drug Repositioning Method Using Meta-Path Aggregating via Hierarchical Attention Mechanism

Drug repositioning is an efficient drug discovery method for identifying associations between present drugs and new diseases, offering considerable development time and cost savings. Although existing methods have been widely applied, they fail to fully capture the complex semantics between drugs and diseases, and are deficient in terms of model interpretability. In this paper, we propose a novel method using a hierarchical attention mechanism aggregating meta-path information for drug-disease association prediction (MPHAM), aiming at effectively integrating heterogeneous information from various sources to enhance prediction accuracy and model interpretability. First, considering the wide range of biological interactions between drugs and diseases, we construct a heterogeneous information network (HIN) to utilize data on drugs, proteins, and diseases. Then, we introduce a meta-path-based feature fusion strategy designed to effectively capture the complex semantics between nodes in the network. By defining meta-paths of multiple lengths and types, information about different relationship types is systematically integrated to generate high-quality node feature representations. Furthermore, the feature fusion strategy incorporates a multi-layer attention mechanism that dynamically assigns weights to the contributions of various meta-paths in the feature aggregation process, significantly improving the model's capacity to capture important semantic information. Experimental results demonstrate that MPHAM can effectively predict drug-disease association by integrating complex meta-path information, and the prediction accuracy is better than five state-of-the-art methods. The case studies of three classical drugs further demonstrate the more accurate predictive performance of MPHAM in drug-candidate disease association prediction.