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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.<\/jats:p>","DOI":"10.1093\/bib\/bbae414","type":"journal-article","created":{"date-parts":[[2024,8,5]],"date-time":"2024-08-05T17:47:52Z","timestamp":1722880072000},"source":"Crossref","is-referenced-by-count":50,"title":["HTINet2: herb\u2013target prediction via knowledge graph embedding and residual-like graph neural network"],"prefix":"10.1093","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-1406-9131","authenticated-orcid":false,"given":"Pengbo","family":"Duan","sequence":"first","affiliation":[{"name":"Institute of Medical 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China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Su","sequence":"additional","affiliation":[{"name":"Institute of Medical Intelligence , Department of Artificial Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, , Beijing 100044, China"},{"name":"School of Computer Science & Technology, Beijing Jiaotong University , Department of Artificial Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, , Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuyue","family":"Fan","sequence":"additional","affiliation":[{"name":"Institute of Medical Intelligence , Department of Artificial Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, , Beijing 100044, China"},{"name":"School of Computer Science & Technology, Beijing Jiaotong University , Department of Artificial Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, , Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Dong","sequence":"additional","affiliation":[{"name":"Institute of Medical Intelligence , Department of Artificial Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, , Beijing 100044, China"},{"name":"School of Computer Science & Technology, Beijing Jiaotong University , Department of Artificial Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, , Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fenghui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Medical Intelligence , Department of Artificial Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, , Beijing 100044, China"},{"name":"School of Computer Science & Technology, Beijing Jiaotong University , Department of Artificial Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, , Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianan","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Medical Intelligence , Department of Artificial Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, , Beijing 100044, China"},{"name":"School of Computer Science & Technology, Beijing Jiaotong University , Department of Artificial Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, , Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyan","family":"Xing","sequence":"additional","affiliation":[{"name":"Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences & Peking Union Medical College , Beijing 100193, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiang","family":"Zhu","sequence":"additional","affiliation":[{"name":"Institute of Medical Intelligence , Department of Artificial Intelligence, Beijing Key Lab of Traffic 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