A heterogeneous graph method integrating multi-layer semantics and topological information for improving drug-target interaction prediction.
10.12122/j.issn.1673-4254.2025.11.12
- Author:
Zihao CHEN
1
;
Yanbu GUO
1
;
Shengli SONG
1
;
Quanming GUO
1
;
Dongming ZHOU
2
Author Information
1. College of Software, Zhengzhou University of Light Industry, Zhengzhou 450001, China.
2. School of Electronic Science and Engineering, Hunan University of Information Technology, Changsha 410151, China.
- Publication Type:Journal Article
- Keywords:
drug-target interaction;
gated mechanism;
graph convolutional networks;
heterogeneous networks;
multi-head attention mechanism
- MeSH:
Semantics;
Humans;
Drug Interactions;
Neural Networks, Computer;
Algorithms
- From:
Journal of Southern Medical University
2025;45(11):2394-2404
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVES:To develop a heterogeneous graph prediction method based on the fusion of multi-layer semantics and topological information for addressing the challenges in drug-target interaction prediction, including insufficient modeling of high-order semantic dependencies, lack of adaptive fusion of semantic paths, and over-smoothing of node features.
METHODS:A heterogeneous graph network with multiple types of entities such as drugs, proteins, side effects, and diseases was constructed, and graph embedding techniques were used to obtain low-dimensional feature representations. An adaptive metapath search module was introduced to automatically discover semantic path combinations for guiding the propagation of high-order semantic information. A semantic aggregation mechanism integrating multi-head attention was designed to automatically learn the importance of each semantic path based on contextual information and achieve differentiated aggregation and dynamic fusion among paths. A structure-aware gated graph convolutional module was then incorporated to regulate the feature propagation intensity for suppressing redundant information and redcuing over-smoothing. Finally, the potential interactions between drugs and targets were predicted through an inner product operation.
RESULTS:Compared with existing drug-target interaction prediction methods, the proposed method achieved an average improvement of 3.4% and 2.4%, 3.0% and 3.8% in terms of the area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUPRC) on public datasets, respectively.
CONCLUSIONS:The drug-target interaction prediction method developed in this study can effectively extract complex high-order semantic and topological information from heterogeneous biological networks, thereby improving the accuracy and stability of drug-target interaction prediction. This method provides technical support and theoretical foundation for precise drug target discovery and targeted treatment of complex diseases.
