QSPR analysis of drugs used for treatment of hepatitis via reduced reverse degree-based topological descriptors

Abstract Topological indices refer to numerical values that are structure-invariant and are used to quantify the bonding topology of a molecular graph. The primary objective of studying topological indices is to acquire and modify chemical structure data, thereby establishing a mathematical correlation between structures and physico-chemical properties, bio-activities, and other experimental attributes. Several studies show a high intrinsic correlation between the molecular architectures of pharmaceuticals and their boiling and melting temperatures, as well as other chemical properties. Researchers can discover more about the physical characteristics, chemical stability, and bioactivities of these chemical molecular structures by using topological indices. To compensate for the lack of chemical experiments and to give a theoretical foundation for the production of pharmaceuticals and chemical materials, topological indices on the molecular structure of chemicals/drugs are studied. This study evaluates the chemical structures of medications used to treat hepatitis (A, B, C, D, E and G) based on reduced reverse degree-based topological indices. The success of drug design is influenced by factors such as solubility, metabolic stability, toxicity, permeability, and transporter effects, which are contingent upon the physical and chemical characteristics of the medication. In recent times, computational techniques have gained prominence in the field of hepatitis medication discovery and development. Machine learning is employed by certain systems to assess the effectiveness and adverse effects of medications. The primary focus of this article is to examine the chemical applicability of ten reduced reverse degree-based descriptors in predicting the ten physico-chemical properties for the 16 drugs employed in the treatment of hepatitis.