DHGT-DTI: Advancing drug-target interaction prediction through a dual-view heterogeneous network with GraphSAGE and Graph Transformer.
10.1016/j.jpha.2025.101336
- Author:
Mengdi WANG
1
;
Xiujuan LEI
1
;
Ling GUO
2
;
Ming CHEN
3
;
Yi PAN
4
Author Information
1. School of Computer Science, Shaanxi Normal University, Xi'an, 710119, China.
2. College of Life Sciences, Shaanxi Normal University, Xi'an, 710119, China.
3. College of Information Science and Engineering, Hunan Normal University, Changsha, 410081, China.
4. Faculty of Computer Science and Control Engineering, Shenzhen University of Advanced Technology, Shenzhen, 518055, China.
- Publication Type:Journal Article
- Keywords:
Drug-target interaction (DTI);
Graph Transformer;
Graph sample and aggregate (GraphSAGE);
Heterogeneous network
- From:
Journal of Pharmaceutical Analysis
2025;15(10):101336-101336
- CountryChina
- Language:English
-
Abstract:
Computational approaches for predicting drug-target interactions (DTIs) are pivotal in advancing drug discovery. Current methodologies leveraging heterogeneous networks often fall short in fully integrating both local and global network information. To comprehensively consider network information, we propose DHGT-DTI, a novel deep learning-based approach for DTI prediction. Specifically, we capture the local and global structural information of the network from both neighborhood and meta-path perspectives. In the neighborhood perspective, we employ a heterogeneous graph neural network (HGNN), which extends Graph Sample and Aggregate (GraphSAGE) to handle diverse node and edge types, effectively learning local network structures. In the meta-path perspective, we introduce a Graph Transformer with residual connections to model higher-order relationships defined by meta-paths, such as "drug-disease-drug", and use an attention mechanism to fuse information across multiple meta-paths. The learned features from these dual perspectives are synergistically integrated for DTI prediction via a matrix decomposition method. Furthermore, DHGT-DTI reconstructs not only the DTI network but also auxiliary networks to bolster prediction accuracy. Comprehensive experiments on two benchmark datasets validate the superiority of DHGT-DTI over existing baseline methods. Additionally, case studies on six drugs used to treat Parkinson's disease not only validate the practical utility of DHGT-DTI but also highlight its broader potential in accelerating drug discovery for other diseases.
