Quantitative Structure-Property Relationship Study for Prediction of Boiling Point and Enthalpy of Vaporization of Alkenes
Fatemeh Ghaemdoost, Fatemeh Shafiei · Current Computer - Aided Drug Design · 2020
INTRODUCTION: Quantitative structure-property relationships (QSPRs) models have been widely developed to derive a correlation between chemical structures of molecules to their known properties. In this study, QSPR models have been used on 91 alkenes to develop a robust model for the prediction of enthalpy of vaporization under standard condition (ΔH°vap/kJ.mol-1) and at normal temperature of boiling points (T˚bp /K) of alkenes. METHODS: A training set of 81 structurally diverse alkenes was randomly selected and used to construct QSPR models. These models were optimized using backward-multiple linear regression (MLR) analysis. The genetic algorithm and multiple linear regression analysis (GA-MLR) were used to select the suitable descriptors derived from the Dragon software. RESULTS: The multicollinearity properties of the descriptors contributed in the QSPR models were tested and several methods were used for testing the predictive models power such as Leave-One-Out (LOO) cross-validation(Q2 LOO), the five-fold cross-validation techniques, external validation parameters (Q2F1, Q2F2, Q2F3), the concordance correlation coefficient (CCC) and the predictive parameter R2 m. CONCLUSION: The predictive ability of the models was found to be satisfactory, and the five descriptors in three blocks, namely connectivity, edge adjacency indices and 2D matrix-based descriptors could be used to predict the mentioned properties of alkenes.