Degree-Based and Neighborhood Degree Sum-Based Entropy Measures of Molnupiravir
Шибсанкар Дас, Arti Kumari, Jayjit Barman · Scientific Annals of Computer Science · 2025
An entirely novel virus that causes severe acute respiratory syndrome began to spread around the world at the end of 2019. Since there are no curative treatments to treat the viral infection, efforts are being made globally to stop the coronavirus from spreading. The coronavirus strain is also known as 2019-nCoV and officially identified as SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2). Several distinctive characteristics of graphs have been used to differentiate the construction of entropy-based measurements from the structure of chemical graphs. Graph entropies have emerged as the information-theoretic quantities for quantifying the fundamental information of chemical graphs. The prospective applicability of the graph entropy measure in discrete mathematics, biology and chemistry has drawn attention from the scientific community. In this work, we study the structural properties of one of the known antiviral drugs, Molnupiravir, and introduce the concept of entropy measure for this drug. We evaluate the degree-based and neighborhood degree sum-based entropy by considering the values of topological indices of the chemical compound. Furthermore, we present the graphical representations of these entropy measures. A comparative analysis of degree-based and neighborhood degree sum-based topological indices, along with their associated entropy measures, is presented for Molnupiravir through graphical illustrations using numerical data.