https://doi.org/10.1140/epje/s10189-025-00551-x
Research - Living Systems
QSPR modeling of polychlorinated biphenyls using degree-based molecular descriptors: a comparative study with linear, polynomial, and ridge regression
1
School of Technology, Fuzhou Technology and Business University, 350715, Fuzhou, Fujian, People’s Republic of China
2
Department of Mathematics, Faculty of Science, University of Gujrat, Gujrat, Pakistan
3
Mathematics Department, College of Science, King Saud University, P.O. Box 22452, 11495, Riyadh, Saudi Arabia
4
Department of Natural Sciences and Humanities, University of Engineering and Technology, Lahore (RCET), Lahore, Pakistan
a
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Received:
2
November
2025
Accepted:
22
December
2025
Published online:
29
January
2026
Chemical graph theory provides a mathematical framework for representing molecular structures as graphs, where atoms correspond to vertices and chemical bonds to edges. This approach enables the use of molecular descriptors to extract reliable structural information and model physicochemical properties. In this study, we investigate the use of recently introduced degree-based molecular descriptors including Euler Sombor, elliptic Sombor, reverse Sombor, reverse elliptic Sombor, reverse Euler Sombor, Lanzhou, and ad-hoc Lanzhou indices to model key properties of polychlorinated biphenyls (PCBs). Experimentally reported properties such as melting point, relative retention time, octanol-water partition coefficient, enthalpy of formation, and Henry’s law constant were analyzed. Quantitative structure–property relationship models were developed using linear, polynomial, and ridge regression techniques. The predictive performance of these models was evaluated through comparison of actual and predicted values, cross-validation, and bootstrapping. Results indicate that the selected descriptors, particularly the elliptic Sombor and reverse Euler Sombor indices, exhibit strong correlations with PCB properties, demonstrating their utility in predicting physicochemical behavior. These models hold potential for applications in chemical ecology, environmental risk assessment, and computational molecular design.
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© The Author(s), under exclusive licence to EDP Sciences, SIF and Springer-Verlag GmbH Germany, part of Springer Nature 2026
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
