Evaluation in quantitative structure--property relationship models of structural descriptors derived from...

Area of Science:

• Computational chemistry
• Cheminformatics
• Structure-property relationships

Background:

• Developing novel molecular descriptors is crucial for advancing computational chemistry and cheminformatics.
• Understanding the information content within molecular structures aids in predicting chemical properties.

Purpose of the Study:

• To define and apply novel information-theory operators (U(M), V(M), X(M), Y(M)) for molecular descriptor development.
• To establish robust structure-property models for key physical and chemical properties of alkanes.

Main Methods:

• Information-theory operators were computed from atomic invariants.
• These operators were applied to six distinct molecular matrices: distance (D), reciprocal distance (RD), distance-path (Dp), reciprocal distance-path (RDp), path Szeged (Sz(p)), and reciprocal path Szeged (RSz(p)).
• The derived information-theory indices were integrated with existing topological indices to build predictive models.

Main Results:

• The information-theory operators successfully quantified the information content of molecular matrices.
• Structure-property models were developed for boiling temperature, molar heat capacity, standard Gibbs energy of formation, vaporization enthalpy, refractive index, and density of alkanes.
• The models incorporating information-theory indices demonstrated good predictive performance for all investigated alkane properties.

Conclusions:

• The novel information-theory operators are valuable tools for generating effective molecular descriptors.
• These descriptors significantly enhance the accuracy of structure-property relationship models for alkanes.
• The study highlights the utility of information theory in molecular descriptor development and property prediction.