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Application of machine intelligence technology in the detection of vaccines and medicines for SARS-CoV-2.
Alsharif, M H; Alsharif, Y H; Albreem, M A; Jahid, A; Solyman, A A A; Yahya, K; Alomari, O A; Hossain, M S.
  • Alsharif MH; Department of Electrical Engineering, College of Electronics and Information Engineering, Sejong University, Gwangjin-gu, Seoul, Korea. malsharif@sejong.ac.kr.
Eur Rev Med Pharmacol Sci ; 24(22): 11977-11981, 2020 11.
Article in English | MEDLINE | ID: covidwho-962034
ABSTRACT
Researchers have found many similarities between the 2003 severe acute respiratory syndrome (SARS) virus and SARS-CoV-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 SARS-CoV-2 and the current application of AI in drug repurposing for COVID-19 treatment.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Artificial Intelligence / Drug Repositioning / Drug Development / COVID-19 Drug Treatment Type of study: Prognostic study Topics: Vaccines Limits: Humans Language: English Journal: Eur Rev Med Pharmacol Sci Journal subject: Pharmacology / Toxicology Year: 2020 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Artificial Intelligence / Drug Repositioning / Drug Development / COVID-19 Drug Treatment Type of study: Prognostic study Topics: Vaccines Limits: Humans Language: English Journal: Eur Rev Med Pharmacol Sci Journal subject: Pharmacology / Toxicology Year: 2020 Document Type: Article