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✅   HeteroRepur: Efficient Modeling of Heterogeneous Disease Graph for Depression Drug Repurposing using Heterogeneous Graph Neural Networks

science

Objective: Major Depressive Disorder (MDD) is one of the most prevalent psychiatric disorders, yet existing treatments often result in suboptimal responses and encounter high recurrence rate. Repositioning existing medications, which often significantly reduces the required time and budget, has been demonstrated to be a promising strategy for drug discovery of multiple central nervous system (CNS) disorders. However, an efficient modeling strategy of heterogeneous semantic information among biological networks has yet to be developed for drug repurposing. Material and Methods: In this study, we proposed HeteroRepur, by integrating heterogeneous graph neural networks to learn interaction features of Depression Drug Repurposing Graph (DDRG) and then classify drugs.

BioRxiv     Jan 05, 2025