Multilinear regression analysis of antiviral medications with topological indices

Topological indices are effective tools for modeling and predicting the molecular structure and physicochemical properties of medications, eliminating the need for lengthy laboratory procedures. Topological indices offer the benefit of acting as fundamental numerical indicators in models related to quantitative structure–property relationships (QSPR) and quantitative structure–activity relationships (QSAR). In this research, we investigate degree-based topological indices (TIs) that serve as molecular descriptors for the QSPR analysis of antiviral medications such as Favipiravir, Sertraline, Chloroquine, Ribavirin, Bepridil, Clomifene, Galidesivir, Nilotinib, and Brincidofovir, which affect the interactions between viruses and human proteins. We examine both linear and multilinear QSPR models to analyze the connections between different physical and chemical properties including molecular polarizability (MP), Log P, molecular refractivity (MR), and molecular weight (MW) and the numerical values associated with these drugs. Our results indicate a strong correlation between the topological indices of these antiviral medications and their physical and chemical characteristics.