Mathematical aspects and molecular descriptors for anti-tumour and anti-COVID drugs medications

Abstract

The COVID-19 pandemic has significantly affected both the global economy and human health. COVID-19 is a single-stranded RNA virus that spreads through the inhalation of viral droplets. Its genome encodes four structural proteins: the envelop protein, spike protein, membrane protein, and nucleocapsid protein. The virus's capsid is a protein shell that contains a positive RNA strand that allows it to take control of human cell machinery. Currently, the only treatments for COVID-19 are preventative measures and immunization. Some of the anti-COVID and anti-cancer medications used to combat this virus include Camptothecin Polymer, Hyaluronic Acid-curcumin, and Hydroxychloroquine. Graphs are used extensively in chemical investigation to provide a mathematical model of molecules. The selection of graph invariants, formerly known as molecular structure descriptor's, is the mathematical modelling of chemical compounds’ physio-chemical, pharmacologic, and toxicological aspects. This paper presents a new technique to compute topological indices for three types of chemical networks for a few anti-COVID and anti-cancer medications. Our findings suggest that these indices show a strong association with the acentric factor and entropy, which make them effective in QSPR and QSAR analysis with high accuracy.