Introduction: Quantitative structure- property relationships (QSPRs) models have been widely
developed to derive correlation between chemical structures of molecules to their known properties. In this
study, QSPR models have been carried out on 91 alkenes to develop a robust model for the prediction of
enthalpy of vaporization at standard condition (∆H°vap/kJ.mol-1) and 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 method were used for testing the predictive models power such as Leave-One-Out (LOO) crossvalidation(Q2 LOO), the five-fold cross-validation techniques, external validation parameters (Q2F1, Q2F2,
Q2F3), the concordance correlation coefficient (CCC) and the predictive parameter R2m .
Conclusion: The predictive ability of the models were 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.