https://doi.org/10.1140/epje/s10189-025-00487-2
Regular Article - Living Systems
Machine learning approaches for modeling the physiochemical characteristics of polycyclic aromatic hydrocarbons
1
Department of Mathematics, College of Science, Jazan University, P.O. Box: 114, 45142, Jazan, Kingdom of Saudi Arabia
2
Department of Mathematics, University of Sialkot, 51310, Sialkot, Pakistan
3
Department of Mathematical and Physical Sciences, College of Arts and Sciences, University of Nizwa, 616, Nizwa, Sultanate of Oman
4
Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Kingdom of Saudi Arabia
a ahmadsms@gmail.com, aimam@jazanu.edu.sa
Received:
1
December
2024
Accepted:
1
April
2025
Published online:
3
May
2025
Supervised machine learning methods like random forests and extreme gradient boosting plays an important role in drug development for predicting bioactivity and resolving structure-activity correlations. These approaches use topological descriptors in the study of polycyclic aromatic hydrocarbons that represent molecular structural characteristics to enhance the prediction capacity of quantitative structure–property relationships (QSPR). The objective is to identify the physoichemical properties such as density, boiling point, flash point, enthalpy, polarizability, surface tension, molar volume, molecular weight and complexity that significantly impact physicochemical attributes. The combination of machine learning and QSPR also demonstrates the potential of computational techniques in drug development. Then effective algorithms are constructed to express the link between the eccentricity-based topological indices and the physicochemical characteristics of each of the polycyclic aromatic hydrocarbons, which grows our understanding of their behavior and paves the way for future development of environmental forecasting techniques and toxicological evaluations of polycyclic aromatic hydrocarbons.
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1140/epje/s10189-025-00487-2.
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© The Author(s), under exclusive licence to EDP Sciences, SIF 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.
