Topological indices and data analysis techniques modeling to predict the physicochemical properties of tetracycline antibiotics.

The tetracycline family of drugs is one of the most widely used groups of antibiotics in modern medicine. Topological indices act as a bridge between chemistry and mathematics. The Quantitative Structure-Property Relationship (QSPR) models utilize the molecular structure of compounds to predict the physicochemical properties. In this paper, a computational approach was performed using MATLAB coding and decoding to calculate the Sombor-type topological indices of this group of drugs. The linear regression approach has been used in the quantitative model of structure-property relationships to investigate the relationships between Sombor indices and physicochemical properties. This investigation aims to examine the efficacy of topological indices of the Sombor type in predicting the physicochemical properties of tetracycline numerically. The linear regression analysis concluded that the best predictor for H-bond donors, H-bond acceptors, rotatable bonds, and polar surface area is the modified reduced Sombor index, and the increased Sombor index is effective for polarizability and molecular weight. Furthermore, the best predictor of topological indices for refractivity is the Sombor index.