QSPR Analysis of Alkanes using Transformation Graphs

Abstract

Topological indices serves as an important tool for numerically representing chemical compounds and are widely used in QSPR/QSAR analysis to study their physicochemical and biological properties. In this study, we focus on alkanes and investigate various derived graphs obtained from their molecular graphs. While several derived structures—such as line graphs, middle graphs, subdivision graphs, and total graphs—are commonly employed in QSPR studies, our work emphasizes the use of transformation graphs as an effective tool for structural analysis. We compute and analyze topological indices for these transformation graphs and examine their correlations with some physical properties of alkanes. Linear regression models are then constructed to establish predictive relationships, and their performance is visually represented through graphical analysis. Furthermore, the root mean square error (RMSE) and correlation coefficient is used as a key metric to evaluate the predictive accuracy of the models. The results demonstrate that transformation-graph–based topological indices provide meaningful insights into the property prediction of alkanes.