A linear and non-linear quantitative structure-activity relationship (QSAR) study is presented for modelling and predicting PDE5 inhibitory activity. A data set consisted of 32 derivatives of tetracyclic guanine was used in this study. Statistical analysis techniques, such as Multiple Linear Regression (MLR), Partial Least-Squares (PLS) Regression and Neural Network (NN) were carried out to calibrate and validate QSAR model. Leave one out method was used to get stable MLR-QSAR with high predictivity: r=0.92, r2=0.85, r2 cv=0.75 and comparable value of cross validated correlation coefficient r2cv=0.78 of PLS in order to predict the robustness of the model. The results obtained by forward feed neural network explained the effect of electronic, hydrophobic and topological descriptors on the biological activity.
Anupama Mittal, Mukta Sharma and Aarti Singh
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