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The Journal of Supercomputing
Erschienen in:

18.09.2024

A quadratic regression model to quantify certain latest corona treatment drug molecules based on coindices of M-polynomial

verfasst von: Shahid Zaman, Sadaf Rasheed, Ahmed Alamer

Erschienen in: The Journal of Supercomputing | Ausgabe 19/2024

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Abstract

Medical researches encounter time, cost, solubility, and data challenges in new drug development. Within the realm of theory, chemical graph theory plays a crucial role in drug design. The SARS-CoV-2 pandemic prompts urgent exploration of drugs like favipiravir, baricitinib, fluvoxamine, nirmatrelvir, molnupiravir, lopinavir, and remdesivir. Developing effective treatments of COVID-19 is a top priority for health authorities, as they strive to curb the pandemic’s impact on public health and prevent future outbreaks. This article characterized CoM-polynomial and their derivatives to determine the topological characteristics of several antiviral drugs. Using this approach, we examine physicochemical properties with quadratic regression method. The results indicate significant correlation between the investigated topological coindices and the physicochemical properties of potential antiviral medicines.
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Metadaten
Titel
A quadratic regression model to quantify certain latest corona treatment drug molecules based on coindices of M-polynomial
verfasst von
Shahid Zaman
Sadaf Rasheed
Ahmed Alamer
Publikationsdatum
18.09.2024
Verlag
Springer US
Erschienen in
The Journal of Supercomputing / Ausgabe 19/2024
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-024-06434-w

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