QSPR Modeling of Status-Based Topological Indices with COVID-19 Drugs

Abstract

The severe acute metabolic process syndrome coronavirus-2 (SARS-CoV-2) virus strain causes an unique Corona viral (COVID-19) disease. It was initially documented in the foreign country of China, and it has since spread around the world. Fever, cough, and runny nose are among the symptoms of this illness, which mostly affects the respiratory system. The peculiar thing is that there is no therapy or vaccine available for this sickness. Clinical trials for a corona virus vaccine are still ongoing, with the vaccine likely to be available by 2021. Until then, the only way to deal with or control the virus’s spread is to take preventative precautions. The numerical descriptors of a molecule structure obtained by the molecular graph are known as topological indices. These can be utilized in structure-property relationship (QSPR) and structure-activity relationship (QSAR) investigations to learn a lot about a molecule’s physicochemical and biological properties. Lopinavir, ritonavir, arbidol, thalidomide, chloroquine, hydroxy-chloroquine, theaflavin, and remdesivir are investigated in this research as crucial lights in COVID-19 treatment. For chemical graphs of these medications, some status distance-based topological indices are constructed. Furthermore, these topological indices are used in the QSPR models to estimate some of the medications’ physicochemical properties. The results of the QSPR experiments, which were acquired using the polynomial regression technique, can contribute in the development of new drugs for the treatment of COVID-19.

Keywords:

• Status distance
• COVID-19 drugs
• QSPR analysis

Acknowledgement

The authors are grateful to the anonymous referee for their valuable comments and suggestions that improved this paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).