dc.contributor.author | Alsharif, Mohammed H. | |
dc.contributor.author | Alsharif, Yahia H. | |
dc.contributor.author | Albreem, Mahmoud A. M. | |
dc.contributor.author | Jahid, Abu | |
dc.contributor.author | Solyman, Ahmad Amin Ahmad | |
dc.contributor.author | Yahya, Khalid O. Moh. | |
dc.contributor.author | Alomari, Osama Ahmad | |
dc.contributor.author | Hossain, Md. Sanwar | |
dc.date.accessioned | 2023-08-16T07:36:51Z | |
dc.date.available | 2023-08-16T07:36:51Z | |
dc.date.issued | 2020 | en_US |
dc.identifier.issn | 1128-3602 | |
dc.identifier.uri | https://hdl.handle.net/11363/5352 | |
dc.description.abstract | Researchers have found many
similarities between the 2003 severe acute respiratory syndrome (SARS) virus and SARSCoV-19 through existing data that reveal the
SARS’s cause. Artificial intelligence (AI) learning models can be created to predict drug structures that can be used to treat COVID-19. Despite
the effectively demonstrated repurposed drugs,
more repurposed drugs should be recognized.
Furthermore, technological advancements have
been helpful in the battle against COVID-19. Machine intelligence technology can support this
procedure by rapidly determining adequate and
effective drugs against COVID-19 and by overcoming any barrier between a large number of
repurposed drugs, laboratory/clinical testing,
and final drug authorization. This paper reviews
the proposed vaccines and medicines for SARSCoV-2 and the current application of AI in drug
repurposing for COVID-19 treatment. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | VERDUCI PUBLISHER, VIA GREGORIO VII, ROME 186-00165, ITALY | en_US |
dc.relation.isversionof | 10.26355/eurrev_202011_23860 | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | * |
dc.subject | COVID-19 | en_US |
dc.subject | SARS-CoV-2 | en_US |
dc.subject | Coronavirus | en_US |
dc.subject | Artificial intelligence | en_US |
dc.subject | Machine learning | en_US |
dc.title | Application of machine intelligence technology in the detection of vaccines and medicines for SARS-CoV-2 | en_US |
dc.type | article | en_US |
dc.relation.ispartof | European Review for Medical and Pharmacological Sciences | en_US |
dc.department | Mühendislik ve Mimarlık Fakültesi | en_US |
dc.authorid | https://orcid.org/0000-0001-8579-5444 | en_US |
dc.authorid | https://orcid.org/0000-0002-6464-1101 | en_US |
dc.authorid | https://orcid.org/0000-0002-2881-8635 | en_US |
dc.authorid | https://orcid.org/0000-0002-0792-7031 | en_US |
dc.identifier.volume | 24 | en_US |
dc.identifier.issue | 22 | en_US |
dc.identifier.startpage | 11977 | en_US |
dc.identifier.endpage | 11981 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.institutionauthor | Solyman, Ahmad Amin Ahmad | |
dc.institutionauthor | Yahya, Khalid O. Moh. | |
dc.institutionauthor | Alomari, Osama Ahmad | |