Predictive potential of eigenvalue-based topological molecular descriptors.

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.