On Topological Descriptors and Curvilinear Regression Analysis of Antiviral Drugs Used in COVID-19 Treatment

Ravi, V.

doi:10.1080/10406638.2021.1993941

Cited by 30 publications

(15 citation statements)

References 17 publications

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“…Kirmani et al [59] studied eight antiviral drugs including lopinavir, ritonavir, arbidol, and thalidomide via M-polynomial and NMpolynomials and performed QSPR and QSAR to predict the strength of these topological indices. For recent topological results and QSPR/QSAR on drugs being used in COVID-19 treatment, refer [60][61][62][63]. Very recently, Yang et al [64] consider nonadjacent vertices of molecular graph of COVID-19 drugs to investigate various topological coindices such as F-coindex, first Zagreb coindex, first multiplicative Zagreb coindex, second Zagreb coindex, and second multiplicative Zagreb coindex of hydroxyethyl starch conjugated with hydroxychloroquine used for COVID-19 treatment.…”

Section: Introductionmentioning

confidence: 99%

Topological Coindices and Quantitative Structure-Property Analysis of Antiviral Drugs Investigated in the Treatment of COVID-19

Kirmani

Ali

Ahmad

2022

Journal of Chemistry

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SARS-CoV-2 is a new strain of coronavirus family that has never been previously detected in humans. This has grown into a huge public health issue that has affected people all around the world. Presently, there is no specific antiviral treatment for COVID-19. To tackle the outbreak, a number of drugs are being explored or have been utilized based on past experience. A molecular descriptor (or topological index) is a numerical value that describes a compound’s molecular structure and has been successfully employed in many QSPR/QSAR investigations to represent several physicochemical attributes. In order to determine topological characteristics of graphs, coindices (topological) take nonadjacent pair of vertices into account. In this study, we introduced CoM-polynomial and numerous degree-based topological coindices for several antiviral medicines such as lopinavir, ritonavir remdesivir, hydroxychloroquine, chloroquine, theaflavin, thalidomide, and arbidol which were studied using the CoM-polynomial approach. In the QSPR model, the linear regression approach is used to analyze the relationships between physicochemical properties and topological coindices. The findings show that the topological coindices under investigation have a substantial relationship with the physicochemical properties of possible antiviral medicines in question. As a result, topological coindices may be effective tools for studying antiviral drugs in the future for QSPR analyses.

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Section: Introductionmentioning

confidence: 99%

Topological Coindices and Quantitative Structure-Property Analysis of Antiviral Drugs Investigated in the Treatment of COVID-19

Kirmani

Ali

Ahmad

2022

Journal of Chemistry

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“…QSPR analysis is a ligand-based drug design method that was proposed by Hanch and Fujitha in 1964 [41]. Out of many molecular descriptors, topological indices are extensively used in QSPR analysis pertaining to drug investigations now [42][43][44][45][46][47][48].…”

Section: Qspr Analysismentioning

confidence: 99%

“…Moreover, as we can see from Figures 2-9, the cubic and linear regression models behave almost the same except for boiling point and enthalpy of vaporization. existing models pertaining to degree and reverse degree [30,31,33,37,45]. Moreover, our proposed models for the data set consist of 12 COVID-19 drug compounds, whereas the existing models mostly focus on eight compounds [30,31,37,45] and the recent one has ten compounds [33].…”

Section: Curvilinear Regression Analysismentioning

confidence: 99%

“…We now conclude this section by a comparative analysis of our proposed regression models based on the modified reverse degree over the existing models pertaining to degree and reverse degree [30,31,33,37,45]. Moreover, our proposed models for the data set consist of 12 COVID‐19 drug compounds, whereas the existing models mostly focus on eight compounds [30,31,37,45] and the recent one has ten compounds [33]. In general, our models have greater predicting potential for properties such as polarization, molar volume, heavy atom count, molecular refraction, molar weight, and complexity with correlations close to 0.99.…”

Section: Qspr Analysismentioning

confidence: 99%

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Linear versus cubic regression models for analyzing generalized reverse degree based topological indices of certain latest corona treatment drug molecules

Arockiaraj¹,

Greeni

Kalaam

2023

Int J of Quantum Chemistry

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The major challenges encountered by medical researchers in developing new drugs are time consumption, increased cost, establishing a safety profile for the drugs, poor solubility, and inadequate experimental data. In its theoretical aspects, chemical graph theory plays a vital role in drug design and development by analyzing the structural parameters of molecules. Topological indices aim at the mathematical representation of a molecular structure, which is used to analyze the effectiveness of drugs and enhance the drug development process. In this study, we consider certain recently used drugs such as dexamethasone, molnupiravir, nirmatrelvir, ivermectin, ribavirin, baricitinib, favipiravir, duvelisib, L‐ascorbic acid, sofosbuvir, remdesivir, and pioglitazone for omicron, delta and other variants of coronaviruses. For these drug molecules, we propose a generalized form of reverse degree parameters and compute their associated topological indices with limiting behaviors. We undertake QSPR study on the potential of generalized reverse‐degree indices using linear and cubic regression models.

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“…He examined using linear regression that flash point, polarizability, enthalpy of vaporization, surface tension and molar volume can be predicted by min‐min rodeg index, and using quadratic regression model, flash point and enthalpy of vaporization can be predicted through the first Zagreb index [ 51 ]. Vignesh Ravi et al developed the QSPR between reduced reverse degree‐based indices and physical properties of the antiviral drugs of COVID‐19 [ 52 ] and Kirmani et al developed the QSPR between coindices and physicochemical properties of COVID‐19 drugs [ 53 ].…”

Section: Quality Testing Analysismentioning

confidence: 99%

Quantitative structure–properties relationship analysis of Eigen‐value‐based indices using COVID‐19 drugs structure

Rauf

Hanif

2022

Int J of Quantum Chemistry

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Topological indices are an important method for understanding the fundamental topology of chemical structures. Quantitative structure properties relationship (QSPR) is an analytical approach for breaking down a molecule into a sequence of numerical values that describe the chemical and physical characteristics of the molecule. In this article, we have developed the QSPR analysis between eigenvalue‐based topological indices and physical properties of COVID‐19 drugs to predict the significance level of eigenvalue based indices. We have to use MATLAB for the computation of indices and SPSS for analysis. We show that positive interia index, signless Laplacian Estrada index and Randić energy are the best predictors of molar reactivity, polar surface area and molecular weight, respectively.

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On Topological Descriptors and Curvilinear Regression Analysis of Antiviral Drugs Used in COVID-19 Treatment

Cited by 30 publications

References 17 publications

Topological Coindices and Quantitative Structure-Property Analysis of Antiviral Drugs Investigated in the Treatment of COVID-19

Topological Coindices and Quantitative Structure-Property Analysis of Antiviral Drugs Investigated in the Treatment of COVID-19

Linear versus cubic regression models for analyzing generalized reverse degree based topological indices of certain latest corona treatment drug molecules

Quantitative structure–properties relationship analysis of Eigen‐value‐based indices using COVID‐19 drugs structure

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