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Predictive potential of eigenvalue-based topological molecular descriptors.

Izudin Redžepović1, Boris Furtula2

  • 1Faculty of Science, University of Kragujevac, P. O. Box 60, 34000, Kragujevac, Serbia.

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Summary

Eigenvalue-based molecular descriptors alone cannot predict alkane properties. Including topological indices like the first Zagreb index and molecular features improves prediction models, with Estrada and resolvent energy showing superior performance.

Keywords:
Boiling pointsGraph invariantsHeats of formationMolecular modelingOctanol/water partition coefficients

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Area of Science:

  • Computational chemistry
  • Cheminformatics

Background:

  • Physicochemical properties of alkanes are crucial in various chemical applications.
  • Molecular descriptors derived from graph theory offer a way to predict these properties.
  • Eigenvalue-based descriptors, such as graph energy and Estrada index, have shown promise but require further investigation.

Purpose of the Study:

  • To assess the predictive power of eigenvalue-based topological molecular descriptors for alkane properties.
  • To determine if individual descriptors are sufficient or if combined features are necessary for accurate predictions.
  • To compare the performance of different eigenvalue-based descriptors in predictive models.

Main Methods:

  • Calculated graph energy, Estrada index, resolvent energy, and Laplacian energy for alkanes.
  • Developed predictive models for boiling points, heats of formation, and octanol/water partition coefficients.
  • Incorporated additional molecular descriptors like the first Zagreb index, spectral properties, and molecular features into the models.
  • Statistically evaluated the performance of different predictive models.

Main Results:

  • No single eigenvalue-based descriptor could accurately predict the studied physicochemical properties.
  • Models incorporating the first Zagreb index, number of spectral zeros, and methyl groups significantly improved prediction accuracy.
  • Predictive models utilizing the Estrada index and resolvent energy outperformed those using graph energy and Laplacian energy.
  • The superiority of Estrada and resolvent energy models was particularly evident for predicting octanol/water partition coefficients.

Conclusions:

  • Eigenvalue-based molecular descriptors require complementary topological indices and molecular features for robust prediction of alkane properties.
  • The Estrada index and resolvent energy demonstrate higher predictive potential compared to graph energy and Laplacian energy.
  • The findings provide insights into developing more accurate quantitative structure-property relationship (QSPR) models for alkanes.