Machine learning-based regression analysis of cyclopentane and cyclohexane molecular graphs.

Cyclopentane is used to make high-performance plastics and composites. Cyclohexane is a polymer that has several specialized applications due to its unique properties, such as good chemical resistance, low moisture absorption, and high thermal stability. Despite their industrial relevance, a systematic comparative analysis of degree-based and neighborhood degree-based topological indices and their predictive capability for chemical properties remains limited in the existing literature. In this study, we compute and analyze several fundamental molecular descriptors, including the Zagreb indices, Randić index, atom bond connectivity index, geometric arithmetic index, sum connectivity index, and augmented Zagreb index for the molecular graphs networks of Cyclopentane and Cyclohexane. These indices can be used to correlate various properties of the polymers with the topological indices. The obtained topological indices are employed to establish quantitative structure-property relationships (QSPR) between molecular structure and physicochemical properties of these polymers. To address the lack of predictive modeling in earlier graph-theoretical studies, a machine-learning-based regression models are developed and applied to evaluate the strength of correlation between molecular descriptors and experimental property data. This approach provides a computationally efficient framework for molecular property estimation and offers a foundation for extending advanced learning models to more complex polymer networks.