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

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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]
Copyright of IAENG International Journal of Applied Mathematics is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Molecular Hypergraph Degree Descriptors based QSPR Predictive Model for Toxic Polycyclic Aromatic Hydrocarbons.
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  Data: <searchLink fieldCode="AR" term="%22Wardani+Meganingtyas%2C+Devi+Eka%22">Wardani Meganingtyas, Devi Eka</searchLink><relatesTo>1</relatesTo><i> deviekawm@unj.ac.id</i><br /><searchLink fieldCode="AR" term="%22Venkatraman%2C+Swaminathan%22">Venkatraman, Swaminathan</searchLink><relatesTo>2</relatesTo><i> swaminathan@src.sastra.edu</i><br /><searchLink fieldCode="AR" term="%22Sridharan%2C+Supriya%22">Sridharan, Supriya</searchLink><relatesTo>2</relatesTo><i> supriya@src.sastra.ac.in</i><br /><searchLink fieldCode="AR" term="%22Dafik%22">Dafik</searchLink><relatesTo>3</relatesTo><i> d.dafik@unej.ac.id</i><br /><searchLink fieldCode="AR" term="%22Agustin%2C+Ika+Hesti%22">Agustin, Ika Hesti</searchLink><relatesTo>4</relatesTo><i> ikahesti.fmipa@unej.ac.id</i><br /><searchLink fieldCode="AR" term="%22Marsidi%22">Marsidi</searchLink><relatesTo>5</relatesTo><i> mar-sidiarin@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Kusbudiono%22">Kusbudiono</searchLink><relatesTo>6</relatesTo><i> kusbudiono.fmipa@unej.ac.id</i><br /><searchLink fieldCode="AR" term="%22Wildhatul+Jannah%2C+Excelsa+Suli%22">Wildhatul Jannah, Excelsa Suli</searchLink><relatesTo>7</relatesTo><i> excelsawildhatul@gmail.com</i>
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  Data: <searchLink fieldCode="JN" term="%22IAENG+International+Journal+of+Applied+Mathematics%22">IAENG International Journal of Applied Mathematics</searchLink>. Aug2026, Vol. 56 Issue 8, p3121-3128. 8p.
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  Data: <searchLink fieldCode="DE" term="%22Polycyclic+aromatic+hydrocarbons%22">Polycyclic aromatic hydrocarbons</searchLink><br /><searchLink fieldCode="DE" term="%22Hypergraphs%22">Hypergraphs</searchLink><br /><searchLink fieldCode="DE" term="%22QSAR+models%22">QSAR models</searchLink><br /><searchLink fieldCode="DE" term="%22Molecular+graphs%22">Molecular graphs</searchLink><br /><searchLink fieldCode="DE" term="%22Nonlinear+regression%22">Nonlinear regression</searchLink><br /><searchLink fieldCode="DE" term="%22Molecular+connectivity+index%22">Molecular connectivity index</searchLink>
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  Data: 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]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of IAENG International Journal of Applied Mathematics is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Text: English
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        PageCount: 8
        StartPage: 3121
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      – SubjectFull: Polycyclic aromatic hydrocarbons
        Type: general
      – SubjectFull: Hypergraphs
        Type: general
      – SubjectFull: QSAR models
        Type: general
      – SubjectFull: Molecular graphs
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      – SubjectFull: Nonlinear regression
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      – SubjectFull: Molecular connectivity index
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    Titles:
      – TitleFull: Molecular Hypergraph Degree Descriptors based QSPR Predictive Model for Toxic Polycyclic Aromatic Hydrocarbons.
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            – D: 01
              M: 08
              Text: Aug2026
              Type: published
              Y: 2026
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