QSPR Models to Predict Thermodynamic Properties of Cycloalkanes Using Molecular Descriptors and GA-MLR Method

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.

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.

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.

The resulting quantitative GA-MLR models of Cv, S, and Eth were obtained:[r2=0.950, Q2=0.989, r2 ext=0.969, MAE(overall,5-flod)=0.6825 Jmol-1K-1], [r2=0.980, Q2=0.947, r2 ext=0.943, MAE(overall,5-flod)=0.5891Jmol-1K-1], 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.