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Peer-reviewedCC BYBoletim da Sociedade Paranaense de Matemática

QSPR Analysis with Curvilinear Regression Modeling and Temperature-Based Topological Indices

  • K. Kallesh1
  • Vijaya Chandra Kumar2
  • Nagesh H. M.3
  • N. Narahari4
4 affiliations
  1. 1SJC Institute of Technology
  2. 2REVA University
  3. 3PES University
  4. 4University College of Science
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Abstract

Establishing quantitative correlations between various molecular properties and chemical structures is of great importance for environmental and medical aspects. These approaches are referred to as Quantitative Structure-Property Relationships (QSPR), which relate the physicochemical or thermodynamic properties of compounds to their structures. The main goal of QSPR studies is to find a mathematical relationship between the property of interest and several molecular descriptors derived from the structure of the molecule. Topological indices are the molecular descriptors that characterize the formation of chemical compounds and predict certain physicochemical properties. In this study, the QSPR models are designed using certain temperature-based topological indices such as the sum connectivity temperature index, product connectivity temperature index, F-temperature index, and symmetric division temperature index to predict the thermodynamic properties, such as enthalpies of formation (Δ H0f \hspace{1mm} liquid), enthalpies of combustion (Δ H0C \hspace{1mm} liquid), and enthalpies of vaporization (Δ H0vap \hspace{1mm} gas) of monocarboxylic acids (C2H4O2 - C20H40O2). The relationship analysis between thermodynamic properties and topological indices is done using linear, quadratic, and cubic equations of a curvilinear regression model. These regression models are then compared.

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Cite

BibTeXRIS
BibTeX
@article{kallesh2026qspr,
  title = {{QSPR Analysis with Curvilinear Regression Modeling and Temperature-Based Topological Indices}},
  author = {K. Kallesh and Vijaya Chandra Kumar and Nagesh H. M. and N. Narahari},
  journal = {Boletim da Sociedade Paranaense de Matemática},
  year = {2026},
  month = sep,
  doi = {10.5269/bspm.80247},
  url = {https://doi.org/10.5269/bspm.80247},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - QSPR Analysis with Curvilinear Regression Modeling and Temperature-Based Topological Indices
AU  - K. Kallesh
AU  - Vijaya Chandra Kumar
AU  - Nagesh H. M.
AU  - N. Narahari
PY  - 2026
DA  - 2026/09/25
JO  - Boletim da Sociedade Paranaense de Matemática
DO  - 10.5269/bspm.80247
UR  - https://doi.org/10.5269/bspm.80247
AB  - Establishing quantitative correlations between various molecular properties and chemical structures is of great importance for environmental and medical aspects. These approaches are referred to as Quantitative Structure-Property Relationships (QSPR), which relate the physicochemical or thermodynamic properties of compounds to their structures. The main goal of QSPR studies is to find a mathematical relationship between the property of interest and several molecular descriptors derived from the structure of the molecule. Topological indices are the molecular descriptors that characterize the formation of chemical compounds and predict certain physicochemical properties. In this study, the QSPR models are designed using certain temperature-based topological indices such as the sum connectivity temperature index, product connectivity temperature index, F-temperature index, and symmetric division temperature index to predict the thermodynamic properties, such as enthalpies of formation (Δ H^0_f \hspace{1mm} liquid), enthalpies of combustion (Δ H^0_C \hspace{1mm} liquid), and enthalpies of vaporization (Δ H^0_vap \hspace{1mm} gas) of monocarboxylic acids (C_2H_4O_2 - C_20H_40O_2). The relationship analysis between thermodynamic properties and topological indices is done using linear, quadratic, and cubic equations of a curvilinear regression model. These regression models are then compared.
SN  - 0037-8712
SN  - 2175-1188
ER  -