Molecular Hypergraph Degree Descriptors based QSPR Predictive Model for Toxic Polycyclic Aromatic Hydrocarbons.

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Bibliographic Details
Title:	Molecular Hypergraph Degree Descriptors based QSPR Predictive Model for Toxic Polycyclic Aromatic Hydrocarbons.
Authors:	Wardani Meganingtyas, Devi Eka¹ deviekawm@unj.ac.id, Venkatraman, Swaminathan² swaminathan@src.sastra.edu, Sridharan, Supriya² supriya@src.sastra.ac.in, Dafik³ d.dafik@unej.ac.id, Agustin, Ika Hesti⁴ ikahesti.fmipa@unej.ac.id, Marsidi⁵ mar-sidiarin@gmail.com, Kusbudiono⁶ kusbudiono.fmipa@unej.ac.id, Wildhatul Jannah, Excelsa Suli⁷ excelsawildhatul@gmail.com
Source:	IAENG International Journal of Applied Mathematics. Aug2026, Vol. 56 Issue 8, p3121-3128. 8p.
Subjects:	Polycyclic aromatic hydrocarbons, Hypergraphs, QSAR models, Molecular graphs, Nonlinear regression, Molecular connectivity index
Abstract:	Polycyclic aromatic hydrocarbons are important compounds in pharmaceuticals, lubricants, agricultural products, thermosetting plastics, and other chemical endeavours. The study of the complex relationships and properties of polycyclic aromatic hydrocarbons (PAHs), by combining theoretical analysis with statistical methods, provides a critical understanding for material design and industrial processes. Topological indices find extensive use in Quantitative Structure–Property Relationship (QSPR) investigations, providing a means to evaluate the physicochemical attributes of chemical compounds. The predictive potential of chemical compound properties using hypergraph representation has not been addressed thus far. In this study, we focus on a dataset of 22 benzenoid hydrocarbons, which are a subclass of PAHs. This paper presents a new approach for representing benzenoid hydrocarbons as hypergraphs utilizing Clar aromatic sextet theory and defines degree-based hypergraph invariants to assess their predictive capability. The adoption of hypergraph representation proves to be more efficient than traditional graph methods in terms of computational cost for calculating indices. The curvilinear regression approach is used to analyze the correlation between physicochemical parameters and degree-based descriptors. It is observed that the cubic regression model demonstrates a stronger association between physicochemical features and the indices. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

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Abstract:	Polycyclic aromatic hydrocarbons are important compounds in pharmaceuticals, lubricants, agricultural products, thermosetting plastics, and other chemical endeavours. The study of the complex relationships and properties of polycyclic aromatic hydrocarbons (PAHs), by combining theoretical analysis with statistical methods, provides a critical understanding for material design and industrial processes. Topological indices find extensive use in Quantitative Structure–Property Relationship (QSPR) investigations, providing a means to evaluate the physicochemical attributes of chemical compounds. The predictive potential of chemical compound properties using hypergraph representation has not been addressed thus far. In this study, we focus on a dataset of 22 benzenoid hydrocarbons, which are a subclass of PAHs. This paper presents a new approach for representing benzenoid hydrocarbons as hypergraphs utilizing Clar aromatic sextet theory and defines degree-based hypergraph invariants to assess their predictive capability. The adoption of hypergraph representation proves to be more efficient than traditional graph methods in terms of computational cost for calculating indices. The curvilinear regression approach is used to analyze the correlation between physicochemical parameters and degree-based descriptors. It is observed that the cubic regression model demonstrates a stronger association between physicochemical features and the indices. [ABSTRACT FROM AUTHOR]
ISSN:	19929978