HTINet2: herb–target prediction via knowledge graph embedding and residual-like graph neural network

计算机科学 人工智能 深度学习 图形 嵌入 机器学习 残余物 知识图 知识库 理论计算机科学 算法
作者
Pengbo Duan,Kuo Yang,Xin‐zhuan Su,Shuyue Fan,Xin Luna Dong,Fenghui Zhang,Xianan Li,Xiaoyan Xing,Qiang Zhu,Jian Yu,Xuezhong Zhou
出处
期刊:Briefings in Bioinformatics [Oxford University Press]
卷期号:25 (5) 被引量:3
标识
DOI:10.1093/bib/bbae414
摘要

Abstract Target identification is one of the crucial tasks in drug research and development, as it aids in uncovering the action mechanism of herbs/drugs and discovering new therapeutic targets. Although multiple algorithms of herb target prediction have been proposed, due to the incompleteness of clinical knowledge and the limitation of unsupervised models, accurate identification for herb targets still faces huge challenges of data and models. To address this, we proposed a deep learning-based target prediction framework termed HTINet2, which designed three key modules, namely, traditional Chinese medicine (TCM) and clinical knowledge graph embedding, residual graph representation learning, and supervised target prediction. In the first module, we constructed a large-scale knowledge graph that covers the TCM properties and clinical treatment knowledge of herbs, and designed a component of deep knowledge embedding to learn the deep knowledge embedding of herbs and targets. In the remaining two modules, we designed a residual-like graph convolution network to capture the deep interactions among herbs and targets, and a Bayesian personalized ranking loss to conduct supervised training and target prediction. Finally, we designed comprehensive experiments, of which comparison with baselines indicated the excellent performance of HTINet2 (HR@10 increased by 122.7% and NDCG@10 by 35.7%), ablation experiments illustrated the positive effect of our designed modules of HTINet2, and case study demonstrated the reliability of the predicted targets of Artemisia annua and Coptis chinensis based on the knowledge base, literature, and molecular docking.
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