IDEAS home Printed from https://ideas.repec.org/a/hin/jjmath/3785932.html
   My bibliography  Save this article

QSPR Modeling with Topological Indices of Some Potential Drug Candidates against COVID-19

Author

Listed:
  • Özge ÇolakoÄŸlu
  • M. T. Rahim

Abstract

COVID-19, which has spread all over the world and was declared as a pandemic, is a new disease caused by the coronavirus family. There is no medicine yet to prevent or end this pandemic. Even if existing drugs are used to alleviate the pandemic, this is not enough. Therefore, combinations of existing drugs and their analogs are being studied. Vaccines produced for COVID-19 may not be effective for new variants of this virus. Therefore, it is necessary to find the drugs for this disease as soon as possible. Topological indices are the numerical descriptors of a molecular structure obtained by the molecular graph. Topological indices can provide information about the physicochemical properties and biological properties of molecules in the quantitative structure-property relationship (QSPR) and quantitative structure-activity relationship (QSAR) studies. In this paper, some analogs of lopinavir, favipiravir, and ritonavir drugs that have the property of being potential drugs against COVID-19 are studied. QSPR models are studied using linear and quadratic regression analysis with topological indices for enthalpy of vaporization, flash point, molar refractivity, polarizability, surface tension, and molar volume properties of these analogs.

Suggested Citation

  • Özge ÇolakoÄŸlu & M. T. Rahim, 2022. "QSPR Modeling with Topological Indices of Some Potential Drug Candidates against COVID-19," Journal of Mathematics, Hindawi, vol. 2022, pages 1-9, May.
  • Handle: RePEc:hin:jjmath:3785932
    DOI: 10.1155/2022/3785932
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/jmath/2022/3785932.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/jmath/2022/3785932.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2022/3785932?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:jjmath:3785932. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.