A heterogeneous graph method integrating multi-layer semantics and topological information for improving drug-target interaction prediction
CHEN Zihao
GUO Yanbu
SONG Shengli
GUO Quanming
ZHOU Dongming
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.
Keywords：drug-target interactionheterogeneous networksgated mechanismmulti-head attention mechanismgraph convolutional networks
Publication Date：2025-11-20
Online Publishing Date：2025-12-05(First online date of this platform, not the publication date of the document)
Pages：11( 2394-2404 )