QSPR/QSAR analysis of some eccentricity based topological descriptors of antiviral drugs used in COVID-19 treatment via $ \mathscr{D}\varepsilon $- polynomials

Deepalakshmi Sarkarai; Kalyani Desikan; Deepalakshmi Sarkarai; Kalyani Desikan

doi:10.3934/mbe.2023769

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 9: 17272-17295. doi: 10.3934/mbe.2023769

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Research article

QSPR/QSAR analysis of some eccentricity based topological descriptors of antiviral drugs used in COVID-19 treatment via $ \mathscr{D}\varepsilon $- polynomials

Deepalakshmi Sarkarai ,
Kalyani Desikan ^,

Division of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, India

Received: 06 May 2023 Revised: 24 July 2023 Accepted: 08 August 2023 Published: 05 September 2023

In the field of chemical and medical sciences, topological indices are used to study the chemical, biological, clinical, and therapeutic aspects of pharmaceuticals. The COVID-19 pandemic is largely recognized as the most life-threatening crisis confronting medical advances. Scientists have tested various antiviral drugs and discovered that they help people recover from viral infections like COVID-19. Antiviral medications, such as Arbidol, Chloroquine, Hydroxy-Chloroquine, Lopinavir, Remdesivir, Ritonavir, Thalidomide and Theaflavin, are often used to treat COVID-19. In this paper, we define Diameter Eccentricity Based vertex degree and employ it to introduce a new polynomial called $ D\varepsilon- $ Polynomial. Using the newly introduced polynomial, we derive new topological indices, namely, diameter eccentricity based and hyper diameter eccentricity based indices. In order to check the efficacy of our indices, we derive the $ D\varepsilon- $ polynomials for the eight COVID-19 drugs mentioned above. Using these polynomials, we compute our proposed topological descriptors for the eight COVID-19 drugs. We perform quantitative structure-property relationship (QSPR) analysis by identifying the best fit curvilinear/multilinear regression models based on our topological descriptors for 8 physico- chemical properties of the COVID-19 drugs. We also perform quantitative structure-activity relationship (QSAR) analysis by identifying the best fit multilinear regression model for predicting the $ IC_{50} $ values for the eight COVID-19 drugs. Our findings and models may be useful in the development of new COVID-19 medication.
- diameter,
- eccentricity,
- molecular descriptors,
- QSPR/QSAR,
- anti-viral drugs
Citation: Deepalakshmi Sarkarai, Kalyani Desikan. QSPR/QSAR analysis of some eccentricity based topological descriptors of antiviral drugs used in COVID-19 treatment via $ \mathscr{D}\varepsilon $- polynomials[J]. Mathematical Biosciences and Engineering, 2023, 20(9): 17272-17295. doi: 10.3934/mbe.2023769

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Abstract

In the field of chemical and medical sciences, topological indices are used to study the chemical, biological, clinical, and therapeutic aspects of pharmaceuticals. The COVID-19 pandemic is largely recognized as the most life-threatening crisis confronting medical advances. Scientists have tested various antiviral drugs and discovered that they help people recover from viral infections like COVID-19. Antiviral medications, such as Arbidol, Chloroquine, Hydroxy-Chloroquine, Lopinavir, Remdesivir, Ritonavir, Thalidomide and Theaflavin, are often used to treat COVID-19. In this paper, we define Diameter Eccentricity Based vertex degree and employ it to introduce a new polynomial called $ D\varepsilon- $ Polynomial. Using the newly introduced polynomial, we derive new topological indices, namely, diameter eccentricity based and hyper diameter eccentricity based indices. In order to check the efficacy of our indices, we derive the $ D\varepsilon- $ polynomials for the eight COVID-19 drugs mentioned above. Using these polynomials, we compute our proposed topological descriptors for the eight COVID-19 drugs. We perform quantitative structure-property relationship (QSPR) analysis by identifying the best fit curvilinear/multilinear regression models based on our topological descriptors for 8 physico- chemical properties of the COVID-19 drugs. We also perform quantitative structure-activity relationship (QSAR) analysis by identifying the best fit multilinear regression model for predicting the $ IC_{50} $ values for the eight COVID-19 drugs. Our findings and models may be useful in the development of new COVID-19 medication.

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