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.

针对药物和靶标等生物分子的复杂关系, 融合多源异质图和异质门图卷积, 设计一种异质生物图动态表示学习方法HGGCN。该方法利用异质图门卷积的门控通道和特征通道, 自适应建模异质生物图中的互作用模式, 融合增强复杂网络的拓扑结构和语义信息, 实现药物与靶标的知识表示以及药物–靶标互作用信息挖掘。实验结果表明, 所提模型的性能优于现有药物–靶标互作用预测方法, 是一种精准的药物–靶标关联预测工具, 可以支持复杂生物数据建模和疾病精准治疗。