Comparative study of Sombor index and its various versions using regression models for top priority polycyclic aromatic hydrocarbons.

The aromatic compounds having structural configurations with two or more fused benzene rings are the polycyclic aromatic hydrocarbons (PAHs). Topological indices are valuable tools for studying the structure property relationships of PAHs and also helps in predicting various properties and activities. They find applications widely in computational chemistry, drug design and QSPR studies. This article focuses on analysing the potential predictive index for Sombor index (SO), elliptic Sombor index (ESO), Euler Sombor index (EU), reverse Sombor index (RSO), reverse elliptic Sombor index (RESO) and reverse Euler Sombor index (REU) using regression models for top priority 38 PAHs. From the study it is evident that, SO and RSO have proved to be potential predictive indices among the considered degree-based and reverse degree-based indices. The variation of best predictive index with minimal RMSE are plotted for linear, quadratic and cubic regression models for better understanding.

A QSPR analysis and curvilinear regression models for various degree-based topological indices: Quinolone antibiotics.

Topological indices play an essential role in defining a chemical compound numerically and are widely used in QSPR/QSAR analysis. Using this analysis, physicochemical properties of the compounds and the topological indices are studied. Quinolones are synthetic antibiotics employed for treating the diseases caused by bacteria. Across the years, Quinolones have shifted its position from minor drug to a very significant drug to treat the infections caused by bacteria and in the urinary tract. A study is carried out on various Quinolone antibiotic drugs by computing topological indices through QSPR analysis. Curvilinear regression models such as linear, quadratic and cubic regression models are determined for all topological indices. These regression models are depicted graphically by extending for fourth degree and fifth degree models for significant topological indices with its corresponding physical property showing the variation between each model. Various studies have been carried out using linear regression models while this work is extended for curvilinear regression models using a novel concept of finding minimal R M S E . R M S E is a significant measure to find potential predictive index that fits QSAR/QSPR analysis. The goal of R M S E lies in predicting a certain property of a chemical compound based on the molecular structure.