Phosphodiesterase V acts as an attractive target for cardiovascular, Quantitative Structure–Activity Relationship (QSAR) technique is helpful for the optimization of structure requirements of twenty-six 1-(2-ethoxyethyl)-1Hpyrazolo[ 4,3-d]pyrimidines as phosphodiesterase V inhibitors. This work was conducted using the principal component analysis (PCA) method, the multiple linear regression method (MLR), the multiple non-linear regressions (MNLR) and the artificial neural network (ANN). The predicted results of various study compounds afford reliable prediction of IC50 with respect to experimental data. Density functional theory (DFT) calculations have been carried out in order to get insights into the structure, chemical reactivity and property information for the series of study compounds. This study shows that the PCA, MLR and ANN have served also to predict activities, but when compared with the results given by the RNLM, we realized that the predictions fulfilled by this latter were more effective.
