{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T16:41:06Z","timestamp":1771605666061,"version":"3.50.1"},"reference-count":85,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2022,5,4]],"date-time":"2022-05-04T00:00:00Z","timestamp":1651622400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,9,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>Coronavirus disease 2019 (COVID-19) has spurred a boom in uncovering repurposable existing drugs. Drug repurposing is a strategy for identifying new uses for approved or investigational drugs that are outside the scope of the original medical indication.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Current works of drug repurposing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are mostly limited to only focusing on chemical medicines, analysis of single drug targeting single SARS-CoV-2 protein, one-size-fits-all strategy using the same treatment (same drug) for different infected stages of SARS-CoV-2. To dilute these issues, we initially set the research focusing on herbal medicines. We then proposed a heterogeneous graph embedding method to signaled candidate repurposing herbs for each SARS-CoV-2 protein, and employed the variational graph convolutional network approach to recommend the precision herb combinations as the potential candidate treatments against the specific infected stage.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Method<\/jats:title>\n                  <jats:p>We initially employed the virtual screening method to construct the \u2018Herb-Compound\u2019 and \u2018Compound-Protein\u2019 docking graph based on 480 herbal medicines, 12,735 associated chemical compounds and 24 SARS-CoV-2 proteins. Sequentially, the \u2018Herb-Compound-Protein\u2019 heterogeneous network was constructed by means of the metapath-based embedding approach. We then proposed the heterogeneous-information-network-based graph embedding method to generate the candidate ranking lists of herbs that target structural, nonstructural and accessory SARS-CoV-2 proteins, individually. To obtain precision synthetic effective treatments forvarious COVID-19 infected stages, we employed the variational graph convolutional network method to generate candidate herb combinations as the recommended therapeutic therapies.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>There were 24 ranking lists, each containing top-10 herbs, targeting 24 SARS-CoV-2 proteins correspondingly, and 20 herb combinations were generated as the candidate-specific treatment to target the four infected stages. The code and supplementary materials are freely available at https:\/\/github.com\/fanyang-AI\/TCM-COVID19.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bib\/bbac124","type":"journal-article","created":{"date-parts":[[2022,3,21]],"date-time":"2022-03-21T12:08:20Z","timestamp":1647864500000},"source":"Crossref","is-referenced-by-count":8,"title":["Signaling repurposable drug combinations against COVID-19 by developing the heterogeneous deep herb-graph method"],"prefix":"10.1093","volume":"23","author":[{"given":"Fan","family":"Yang","sequence":"first","affiliation":[{"name":"The Department of Epidemiology and Biostatistics , School of Public Health, Cheeloo College of Medicine, Shandong University, China"}]},{"given":"Shuaijie","family":"Zhang","sequence":"additional","affiliation":[{"name":"The Department of Epidemiology and Biostatistics , School of Public Health, Cheeloo College of Medicine, Shandong University, China"}]},{"given":"Wei","family":"Pan","sequence":"additional","affiliation":[{"name":"The Department of Epidemiology and Biostatistics , School of Public Health, Cheeloo College of Medicine, Shandong University, China"}]},{"given":"Ruiyuan","family":"Yao","sequence":"additional","affiliation":[{"name":"Shandong University of Traditional Chinese Medicine , Jinan, China"}]},{"given":"Weiguo","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shandong University of Traditional Chinese Medicine , Jinan, China"}]},{"given":"Yanchun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute for Sustainable Industries & Liveable Cities , Victoria University, Australia; The Department of New Networks, Peng Cheng Laboratory, Shenzhen, China"}]},{"given":"Guoyin","family":"Wang","sequence":"additional","affiliation":[{"name":"Chongqing Key Laboratory of Computational Intelligence , Chongqing University of Posts and Telecommunications, Chongqing, 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