HeteroRepur: Efficient Modeling of Heterogeneous Disease Graph for Depression Drug Repurposing using Heterogeneous Graph Neural Networks
Year: 2025
Type: preprint
Abstract: 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. Results The DDRG embeddings learned by HeteroRepur accurately capture graph information, including 89 types of interaction or associations among drugs, genes, pathways, and GOs, as well as disease genetic network of MDD. In drug classification task, HeteroRepur outperforms various baseline models, including existing graph neural network approaches that do not consider heterogeneous network information. Finally, HeteroRepur predicts new candidates for treating MDD, including CNS drugs that have the potential to be extended to MDD, and dietary supplements that might benefit depression patients by elevating essential nutrient levels. Conclusion HeteroRepur outperforms traditional graph-based approaches, and can be used to rank promising drugs among complex disease networks with heterogeneous topology.
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G2584881318, G2608660931, Recruitment of Rising Stars, Open Health Natural Language Processing Collaboratory, Finding combinatorial drug repositioning therapy for Alzheimer's disease and related dementias, G4972693717, G5388406022, Collaborative Research: SaTC: CORE: Medium: PREMED: Privacy-Preserving and Robust Computational Phenotyping using Multisite EHR Data, G6515066292, G6646588417, G7048620842, G7757262149, Biomedical Informatics, Genomics, and Translational Cancer Research Training Program (BIG-TCR), Harmonizing multiple clinical trials for Alzheimer's disease to investigate differential responses to treatment via federated counterfactual learning, G8770013317, Decentralized differentially-private methods for dynamic data release and analysis
OpenAlex ID: W4406064806