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The study focused on QSPR (Quantitative Structure-Property Relationship) analysis using a variety of topological indices on the medications Mefloquinone, Sertraline, Niclosamide, Tizoxanide, PHA-690509, Ribavirin, Emricasan, and Sofosbuvir. Through the use of computational modeling approaches, the study sought to determine how these medications' chemical structures relate to their individual qualities. The discovered results provided information on the quantitative correlations between structural characteristics and pharmacological qualities, allowing for better comprehension and forecast of their behavior. The results of this study make a positive contribution to the field of medication discovery and design by offering important knowledge about the structure-property correlations of these medicinal molecules. In this article, we focus on using topological indices and a linear regression model to successfully predict various pharmacological features. This method enables more effective drug discovery and development by providing insights into the connection between molecular structure and pharmacological characteristics. We can improve our comprehension of drug behavior and assist targeted drug design by utilizing topological indices and regression analysis. [ABSTRACT FROM AUTHOR]

Several graph features have been used to distinguish the construction of entropy-based measurements from the structure of chemical graphs and complicated networks. The graph entropy metric has piqued the interest of scientists due to its possible applicability in a variety of domains. In this paper, we look at the chemical graph of silicon–carbon SiC3-I and the crystallographic structure of silicon–carbon SiC3-II. This two-dimensional structure has the potential to revolutionize optoelectronic and electrical technologies as a direct wide band gap semiconducting material. More intriguingly, the semiconducting SiC3 sheet has a significant visible-light adsorption ability. SiC3 nanosheets have a wide range of electrical characteristics, making them ideal for nano-electronics and photo-voltaic. This advantageous molecular structure, silicon carbon, is examined. More preciously in this paper, we have computed the entropy measures for SiC3-I and SiC3-II based on the topologies indices. Some comparison work is offered in addition to the analytical analysis of the entropy measure of silicon–carbon. Bases on these numerical tables and corresponding graphs we conclude that as entropy measures are the better predicator than the topological indices of physio-chemical properties. [ABSTRACT FROM AUTHOR]