Aims and Objectives: QSPR models establish relationships between different types of structural
information to their observed properties. In the present study the relationship between the molecular descriptors
and quantum properties of cycloalkanes is represented.
Materials and Methods: Genetic Algorithm (GA) and Multiple Linear Regressions (MLR) were successfully
developed to predict quantum properties of cycloalkanes. A large number of molecular descriptors were
calculated with Dragon software and a subset of calculated descriptors was selected with a genetic algorithm
as a feature selection technique. The quantum properties consist of the heat capacity (Cv)/ Jmol-1K-1
entropy(S)/ Jmol-1K-1 and thermal energy(Eth)/ kJmol-1 were obtained from quantum-chemistry technique at
the Hartree-Fock (HF) level using the ab initio 6-31G* basis sets.
Results: The Genetic Algorithm (GA) method was used to select important molecular descriptors and then
they were used as inputs for SPSS software package. The predictive powers of the MLR models were discussed
using Leave-One-Out (LOO) cross-validation, leave-group (5-fold)-out (LGO) and external prediction
series. The statistical parameters of the training and test sets for GA–MLR models were calculated.
Conclusion: The resulting quantitative GA-MLR models of Cv, S, and Eth were obtained:[r2=0.950, Q2=0.989,
ext=0.969, MAE(overall,5-flod)=0.6825 Jmol-1K-1], [r2=0.980, Q2=0.947, r2
and [r2=0.980, Q2=0.809, r2
ext=0.985, MAE(overall,5-flod)=2.0284 kJmol-1]. The results showed that the predictive
ability of the models was satisfactory, and the constitutional, topological indices and ring descriptor
could be used to predict the mentioned properties of 103 cycloalkanes.