https://doi.org/10.1140/epjp/s13360-024-05939-w
Regular Article
On some neighborhood degree-based structure descriptors and their applications to graphene
1
Department of Mathematics, Dayananda Sagar University, Bengaluru, Karnataka, India
2
Department of Mathematics, National Institute of Technology Durgapur, Durgapur, West Bengal, India
3
Research Institute of Sciences and Engineering (RISE), Research Group MASEP, University of Sharjah, 27272, Sharjah, UAE
Received:
18
August
2024
Accepted:
19
December
2024
Published online:
9
January
2025
Topological indices are numerical descriptors derived from the structure of molecular graphs, capturing key aspects of their topology and connectivity. They play a vital role in cheminformatics by correlating molecular structure with physical, chemical, and biological properties, enabling efficient property predictions and molecular design. In this article, we explored the applicability of several neighborhood degree-based structural descriptors, including the fifth M-Zagreb indices, fifth hyper-M-Zagreb indices, and NDe indices, in predicting key thermodynamic properties of benzenoid hydrocarbons. These indices serve as mathematical tools to capture structural and topological features of molecular graphs, providing valuable insights into molecular behavior. Our analysis demonstrates that these indices exhibit strong correlations with critical thermodynamic properties such as boiling point, entropy, enthalpy of formation, Kovats retention index, and octanol-water partition coefficient of benzenoid hydrocarbons. Among the studied descriptors, the fourth NDe index showed particularly impressive performance, with correlation coefficients exceeding 0.97 for all considered properties. These findings highlight the potential of neighborhood degree-based indices as reliable predictors of molecular properties, offering a cost-effective and computationally efficient alternative to experimental methods. Furthermore, the results underscore the utility of graph-theoretic descriptors in the broader context of cheminformatics and property prediction for organic compounds, paving the way for future research into their applications across diverse molecular systems.
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© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2025
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.
