北京大学学报（自然科学版）2026，Vol.62Issue(2)：286-296,11.DOI:10.13209/j.0479-8023.2025.093
基于异质生物图动态表示学习的药物-靶标关系预测
Drug-target Interaction Prediction Based on Dynamic Representation Learning of Heterogeneous Biological Graphs
摘要
Abstract
To extract the complex relationships between drugs and targets,this paper designs a dynamic representation learning algorithm based on deep heterogeneous graph gated convolutional networks(HGGCN)for biological graph modeling and representation learning.The algorithm combines the merits of the gated channels and feature channels to adaptively model interaction patterns of heterogeneous graphs,enhance the topological structure and semantic information of complex networks based on the fusion,and obtain the discriminative representation of drugs and targets for drug-target interaction mining.Experimental results show that the proposed model outperforms existing drug target interaction prediction methods,and is also an accurate drug target association prediction tool,which could provide the technical support for the precision treatment of complex diseases and network information mining.关键词
复杂生物网络/多源异质图/神经网络/药物-靶标互作用Key words
complex biological networks/multi-source heterogeneous graph/neural networks/drug-target inter-action引用本文复制引用
郭延哺,李维华,曹进德,周冬明..基于异质生物图动态表示学习的药物-靶标关系预测[J].北京大学学报（自然科学版）,2026,62(2):286-296,11.基金项目
国家自然科学基金(62403437,62576098)、河南省重点研发与推广专项(242102211039)和郑州轻工业大学校级青年骨干教师培养项目(13502010009)资助 （62403437,62576098）
