Abstarct: In the first section of this project, quantitative structure-activity relationship (QSAR) study was conducted on the inhibition effect (pIC50) of 40 drug compounds of Arylalkoxyphenylalkylamine derivatives as inhibition of schizophrenia. The stepwise multiple linear regression and genetic algorithm methods were used to select the most important descxriptors. Multiple linear regression (MLR) as a linear method and artificial neural network (ANN) as nonlinear method were used to predicting the inhibition effect of these compounds. The validation of the MLR and ANN models was performed using test set, leav-one-out and Y-randomization techniques. The obtained results are shown, determination coefficients for prediction of inhibition effect of the test set by SR-MLR, SR-ANN and GA-ANN models were 0/918, 0/951 and 0/835 respectively. In the second part of this project, quantitative structure-property relationship (QSPR) study was developed to predict of the retention index (RI) of some organic compounds. The stepwise multiple linear regression and genetic algorithm methods were used to select the most important descxriptors. Multiple linear regression (MLR) as a linear method and artificial neural network (ANN) as nonlinear method used to predicting the retention index. The validation of the MLR and ANN models was performed using test set, leave-group-out and Y-randomization techniques. The obtained results are shown, determination coefficients for prediction of retention index of the test set by SR-MLR, SR-ANN and GA-ANN models were 0/959, 0/999 and 0/999 respectively.