A Paradigmatic Approach for Physico-Chemical Characterization of COVID-19 Drugs via ST-Polynomial

Abstract

One of the most infectious diseases in recorded human history, Coronavirus Disease-19 (COVID-19), is a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-related illness that has already claimed millions of lives throughout the globe. Neither vaccines nor effective medicines are currently available that can effectively treat COVID-19 patients or stop the virus's transmission. The development of medications and vaccinations may take a long time, but scientific communities all around the world have responded quickly and have been working nonstop on them. Repurposing the already available antiviral medications may be the best course of action in the face of this uncertainty to hasten the development of efficient treatments for SARS-CoV-2. Drug repurposing may also provide important information on druggable targets, which might be used to develop new drugs using a target-based approach. Updated information is also required on potential pharmacological targets, therapeutic and vaccine development, and results. The design and analysis of approximation techniques for graph partitioning issues, the investigation of random walks in graphs, and the creation of expander graphs are all applications of spectral graph theory. For numerous COVID-19 antiviral medications, we suggest status distance-based polynomials and topological descriptors in this work. We also examine the suggested topological indices' Quantitative Structure-Property Relationship (QSPR). Curve fitting models for the physico-chemical properties of the COVID-19 drugs are obtained and looked at in line with the specified indices. Our models and results may facilitate the development of innovative drugs for the treatment of COVID-19.