Quantitative structure-property relationship (QSPR) modeling utilizes advanced mathematical methods for the prediction of physicochemical properties (PCPs) and quantum-theoretic propertiesQuantum-Theoretic Property (QTP) (QTPs) of chemical compounds. Graph-based descriptors deliver such mathematical tools to effectively correlate PCPs and QTPs of organic structure, especially, benzenoid hydrocarbonsBenzenoid hydrocarbon (BH). This chapter considers a contemporary class of graph-based descriptors known as connection-based descriptors. Computational methods are presented to compute these graphical invariants and then advanced statistical tools are employed to put forward better performing connection-based indices in predicting QTPs of BHs. Correlation and regression analysis is performed on top five connection descriptors for proposing structure-property regression models for efficiently predicting QTPs of benzenoid hydrocarbons.

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Structure-Property Modeling of Quantum-Theoretic Properties of Benzenoid Hydrocarbons by Means of Connection-Related Graphical Descriptors

  • Sakander Hayat

摘要

Quantitative structure-property relationship (QSPR) modeling utilizes advanced mathematical methods for the prediction of physicochemical properties (PCPs) and quantum-theoretic propertiesQuantum-Theoretic Property (QTP) (QTPs) of chemical compounds. Graph-based descriptors deliver such mathematical tools to effectively correlate PCPs and QTPs of organic structure, especially, benzenoid hydrocarbonsBenzenoid hydrocarbon (BH). This chapter considers a contemporary class of graph-based descriptors known as connection-based descriptors. Computational methods are presented to compute these graphical invariants and then advanced statistical tools are employed to put forward better performing connection-based indices in predicting QTPs of BHs. Correlation and regression analysis is performed on top five connection descriptors for proposing structure-property regression models for efficiently predicting QTPs of benzenoid hydrocarbons.