Quality Analysis of COVID-19 Drugs through Graph Polynomial

Abstract

Coronavirus disease-19 (COVID-19), a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-related sickness that has already claimed millions of lives throughout the world, is one of the most contagious illnesses in known human history. At this time, neither vaccines nor effective medicines exist that can effectively cure COVID-19 patients or stop the virus from spreading. Even though the creation of drugs and vaccines may take a while, scientific communities all over the world have reacted rapidly and have been working ceaselessly on them. In light of this uncertainty, repurposing currently existing antiviral drugs may be the best line of action to speed the creation of effective SARS-CoV-2 therapeutics. Updated information is also required on potential pharmacological targets, therapeutic and vaccine development, and results. In this study, we propose topological descriptors and status distance-based polynomials for many COVID-19 antiviral drugs. The quantitative structure–property relationship (QSPR) connection of the proposed topological indices (TIs) is also investigated. Curve fitting models are obtained and looked at for the physico-chemical properties of the COVID-19 drugs in line with the specified indices. Our models and results may contribute to the identification of innovative therapeutics for the treatment of COVID-19.

Keywords:

• Antiviral drugs
• chemical graphs
• curve fitting
• QSPR models
• topological indices

Disclosure statement

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