Prediction of Partition Coefficient of Carbamates using GA-MLR and GA-ANN Methods, and Comparison with Experimental Data

In the present study, quantum mechanics calculations at the B3LYP theory level and 6-31G* basis set were carried out to obtain the optimized geometry of carbamates. Then, a comprehensive set of molecular descriptors was computed by using the Dragon software. A genetic algorithm (GA) was also applied to select the suitable variables that resulted in the best-fixed models. The relationship between the molecular descriptors and the partition coefficient of 66 types of carbamates is represented. The molecular descriptors were applied for modeling the multiple linear regression (MLR) and artificial neural network (ANN) methods. The quantitative structure-property relationship models showed that the GA-ANN over the GA-MLR approach resulted in the best outcome. So, the predicted partition coefficient was found to be in good agreement with the experimental partition coefficient. The EEig01x and ALOGP descriptors were applied for modeling the multiple linear regression (MLR) and artificial neural network (ANN) methods. The best model was validated by Q(LOO)(2), Q(F1)(2), Q(F2)(2), Q(F3)(2), and CCC techniques and external validation parameters for the established theoretical models.