Aim and Objective: Cycloalkanes have been largely used in the field of medicine, components
of food, pharmaceutical drugs, and they are mainly used to produce fuel.
In present study the relationship between molecular descriptors and thermodynamic properties such as the standard enthalpies of formation (∆H°f), the standard enthalpies of fusion (∆H°fus), and the standard Gibbs free energy of formation (∆G°f)of the cycloalkanes is represented.
Materials and Methods: The Genetic Algorithm (GA) and multiple linear regressions (MLR) were
successfully used to predict the thermodynamic properties of cycloalkanes. A large number of molecular
descriptors were obtained with the Dragon program. The Genetic algorithm and backward
method were used to reduce and select suitable descriptors.
Results: QSPR models were used to delineate the important descriptors responsible for the properties
of the studied cycloalkanes. The multicollinearity and autocorrelation properties of the descriptors
contributed in the models were tested by calculating the Variance Inflation Factor (VIF),
Pearson Correlation Coefficient (PCC) and the Durbin–Watson (DW) statistics. The predictive
powers of the MLR models were discussed using Leave-One-Out Cross-Validation (LOOCV) and
test set validation methods. The statistical parameters of the training, and test sets for GA–MLR
models were calculated.
Conclusion: The results of the present study indicate that the predictive ability of the models was
satisfactory and molecular descriptors such as: the Functional group counts, Topological indices,
GETAWAY descriptors, Constitutional indices, and molecular properties provide a promising
route for developing highly correlated QSPR models for prediction the studied properties.