南方医科大学学报2025，Vol.45Issue(11)：2394-2404,11.DOI:10.12122/j.issn.1673-4254.2025.11.12
基于多层语义与拓扑融合的异质图方法提升药物-靶标相互作用预测性能
A heterogeneous graph method integrating multi-layer semantics and topological information for improving drug-target interaction prediction
摘要
Abstract
Objective 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.Conclusion 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.关键词
药物-靶标相互作用/异质网络/门控机制/多头注意力机制/图卷积网络Key words
drug-target interaction/heterogeneous networks/gated mechanism/multi-head attention mechanism/graph convolutional networks引用本文复制引用
陈紫豪,郭延哺,宋胜利,郭全明,周冬明..基于多层语义与拓扑融合的异质图方法提升药物-靶标相互作用预测性能[J].南方医科大学学报,2025,45(11):2394-2404,11.基金项目
国家自然科学基金(62403437,62066047) （62403437,62066047）
河南省科技攻关项目(242102211039) （242102211039）
郑州轻工业大学青年骨干教师培养资助项目(13502010009) Supported by National Natural Science Foundation of China(62403437,62066047) （13502010009）
