Ontological modeling and analysis of experimentally or clinically verified drugs against coronavirus infection

Abstract Our systematic literature collection and annotation identified 106 chemical drugs and 31 antibodies effective against the infection of at least one human coronavirus (including SARS-CoV, SAR-CoV-2, and MERS-CoV) in vitro or in vivo in an experimental or clinical setting. A total of 163 drug...

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Autores principales:	Yingtong Liu, Junguk Hur, Wallace K. B. Chan, Zhigang Wang, Jiangan Xie, Duxin Sun, Samuel Handelman, Jonathan Sexton, Hong Yu, Yongqun He
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/f088e050f20c4123afa354dfa35d4e8c
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spelling	oai:doaj.org-article:f088e050f20c4123afa354dfa35d4e8c2021-12-02T14:02:34ZOntological modeling and analysis of experimentally or clinically verified drugs against coronavirus infection10.1038/s41597-021-00799-w2052-4463https://doaj.org/article/f088e050f20c4123afa354dfa35d4e8c2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41597-021-00799-whttps://doaj.org/toc/2052-4463Abstract Our systematic literature collection and annotation identified 106 chemical drugs and 31 antibodies effective against the infection of at least one human coronavirus (including SARS-CoV, SAR-CoV-2, and MERS-CoV) in vitro or in vivo in an experimental or clinical setting. A total of 163 drug protein targets were identified, and 125 biological processes involving the drug targets were significantly enriched based on a Gene Ontology (GO) enrichment analysis. The Coronavirus Infectious Disease Ontology (CIDO) was used as an ontological platform to represent the anti-coronaviral drugs, chemical compounds, drug targets, biological processes, viruses, and the relations among these entities. In addition to new term generation, CIDO also adopted various terms from existing ontologies and developed new relations and axioms to semantically represent our annotated knowledge. The CIDO knowledgebase was systematically analyzed for scientific insights. To support rational drug design, a “Host-coronavirus interaction (HCI) checkpoint cocktail” strategy was proposed to interrupt the important checkpoints in the dynamic HCI network, and ontologies would greatly support the design process with interoperable knowledge representation and reasoning.Yingtong LiuJunguk HurWallace K. B. ChanZhigang WangJiangan XieDuxin SunSamuel HandelmanJonathan SextonHong YuYongqun HeNature PortfolioarticleScienceQENScientific Data, Vol 8, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Yingtong Liu Junguk Hur Wallace K. B. Chan Zhigang Wang Jiangan Xie Duxin Sun Samuel Handelman Jonathan Sexton Hong Yu Yongqun He Ontological modeling and analysis of experimentally or clinically verified drugs against coronavirus infection
description	Abstract Our systematic literature collection and annotation identified 106 chemical drugs and 31 antibodies effective against the infection of at least one human coronavirus (including SARS-CoV, SAR-CoV-2, and MERS-CoV) in vitro or in vivo in an experimental or clinical setting. A total of 163 drug protein targets were identified, and 125 biological processes involving the drug targets were significantly enriched based on a Gene Ontology (GO) enrichment analysis. The Coronavirus Infectious Disease Ontology (CIDO) was used as an ontological platform to represent the anti-coronaviral drugs, chemical compounds, drug targets, biological processes, viruses, and the relations among these entities. In addition to new term generation, CIDO also adopted various terms from existing ontologies and developed new relations and axioms to semantically represent our annotated knowledge. The CIDO knowledgebase was systematically analyzed for scientific insights. To support rational drug design, a “Host-coronavirus interaction (HCI) checkpoint cocktail” strategy was proposed to interrupt the important checkpoints in the dynamic HCI network, and ontologies would greatly support the design process with interoperable knowledge representation and reasoning.
format	article
author	Yingtong Liu Junguk Hur Wallace K. B. Chan Zhigang Wang Jiangan Xie Duxin Sun Samuel Handelman Jonathan Sexton Hong Yu Yongqun He
author_facet	Yingtong Liu Junguk Hur Wallace K. B. Chan Zhigang Wang Jiangan Xie Duxin Sun Samuel Handelman Jonathan Sexton Hong Yu Yongqun He
author_sort	Yingtong Liu
title	Ontological modeling and analysis of experimentally or clinically verified drugs against coronavirus infection
title_short	Ontological modeling and analysis of experimentally or clinically verified drugs against coronavirus infection
title_full	Ontological modeling and analysis of experimentally or clinically verified drugs against coronavirus infection
title_fullStr	Ontological modeling and analysis of experimentally or clinically verified drugs against coronavirus infection
title_full_unstemmed	Ontological modeling and analysis of experimentally or clinically verified drugs against coronavirus infection
title_sort	ontological modeling and analysis of experimentally or clinically verified drugs against coronavirus infection
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f088e050f20c4123afa354dfa35d4e8c
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Ontological modeling and analysis of experimentally or clinically verified drugs against coronavirus infection

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