Anti Covid-19 Drugs Suggested by Computational Methods: A Review

Year : 2023 | Volume :01 | Issue : 01 | Page : 16-34
By

    B. Reshika

  1. Samiksha Shivaji Bhor

  1. Student, Department of Life Sciences, CHRIST University, Karnataka, India
  2. Bioinformatics Associate, Department of Biotechnology, BioNome, Karnataka, India

Abstract

Background and aims: Long COVID refers to the persistence of illness in individuals who have survived from COVID-19 infection. This article reviews long covid and the use of in silico techniques in drug development for COVID-19. Results: Many COVID-19 survivors have long COVID, which has become a burden in daily life. Various attempts have been undertaken to relieve symptoms and manage it, but there is no cure as of yet. Healthy lifestyle practices and periodic health assessment could be of some help. Many plant phytochemicals, synthetic medicines, and other compounds show tremendous potential in computational analysis and should be studied further in the laboratory employing in vivo and in vitro approaches to understand their biochemistry in the human body. Conclusions: This review will assist investigators in analyzing long COVID and further exploring the given compounds in order to find a cure for COVID-19.

Keywords: SARS-CoV-2; Long COVID; Molecular docking; Pandemic; Drug discovery.

[This article belongs to International Journal of Cell Biology and Cellular Functions(ijcbcf)]

How to cite this article: B. Reshika, Samiksha Shivaji Bhor , Anti Covid-19 Drugs Suggested by Computational Methods: A Review ijcbcf 2023; 01:16-34
How to cite this URL: B. Reshika, Samiksha Shivaji Bhor , Anti Covid-19 Drugs Suggested by Computational Methods: A Review ijcbcf 2023 {cited 2023 Jun 01};01:16-34. Available from: https://journals.stmjournals.com/ijcbcf/article=2023/view=110310


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Regular Issue Subscription Review Article
Volume 01
Issue 01
Received April 13, 2023
Accepted April 29, 2023
Published June 1, 2023
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