QSPR modeling of some COVID-19 drugs using neighborhood eccentricity-based topological indices: A comparative analysis

Abstract

COVID-19, which emerged in 2019, is a disease caused by a new coronavirus, severe acute respiratory syndrome coronavirus 2 (SARSCoV-2), and has caused a worldwide epidemic. During and after this outbreak, it has been confirmed once again that finding a drug to prevent and end such diseases as soon as possible is an important issue. However, drug discovery and to determine a molecule's physical characteristics in a lab takes effort and time and is a costly process. Relevant information about molecules can be obtained by calculating topological indices, which are molecular descriptive numerical values corresponding to the physical properties of the chemical structure of a molecule. In this paper, we consider recently used drugs such as arbidol, chloroquine, hydroxy-chloroquine, lopinavir, remdesivir, ritonavir, thalidomide and theaflavin in treatment of COVID-19. This article examines neighborhood eccentricity-based topological descriptors that are used to analyze the structures of potential drugs against COVID-19. Eccentricity-based topological indices are advancing the field of chem-informatics and helping scientists better understand structure-activity correlations across a wide range of chemical compounds. The purpose is to identify structural components that have a significant impact on physico-chemical properties. In this context, the chemical structure and the corresponding molecular graph of the drugs under consideration are given in order to calculate the neighborhood eccentricity values. QSPR models are studied using linear and cubic regression analysis with topological indices for boiling point, enthalpy of vaporization, flash point, molar refraction, polar surface area, polarizability, molar volume and molecular weight properties of these drugs. Regression analysis is applied to find potential correlation between different drug characteristics such as bio-availability and efficacy. The results show that topological indices and applied regression models are useful in predicting significant characteristics of drugs used for the treatment of COVID-19. Additionally, a comparison of the known values and the calculated values from the regression models discussed is obtained.

Fig 1. The chemical structure and corresponding molecular graph of arbidol, respectively [37].

Fig 2. The chemical structure and corresponding molecular graph of chloroquine, respectively [37].

Fig 3. The chemical structure and corresponding molecular graph of hydroxy-chloroquine, respectively [37].

Fig 4. The chemical structure and corresponding molecular graph of lopinavir, respectively [37].

Fig 5. The chemical structure and corresponding molecular graph of remdesivir, respectively [37].

Fig 6. The chemical structure and corresponding molecular graph of ritonavir, respectively [37].

Fig 7. The chemical structure and corresponding molecular graph of thalidomide, respectively [37].

Fig 8. The chemical structure and corresponding molecular graph of theaflavin, respectively [37].

Fig 9. Correlation of physicochemical properties on TIs.