Exploring QSARs for Inhibitory Activity of some Antimalarial Compounds by MLR and PC-ANN
Alaa Ishaq Hasan Ashour
الاء إسحق حسن عاشور
As malaria disease is continuous to be one of the major health problems, and until now no effective vaccines or drugs are available due to the mutation of the plasmodium. So in order to help in designing a new antimalarial agents, a quantitative structure activity relationship was performed to study the Activity of 79 compound as antimalarial agents. The QSAR models were developed using the multiple linear regression (MLR) as a linear method. Also the principle component –artificial neural network (PC-ANN) was used as nonlinear method for modeling. The models resulted have a good prediction power. The MLR resulted with models (13-17) which have R2 >0.6, the best model was model number 17 with correlation coefficient R= 0.889, R2= 0.791, and R2adj.= 0.733. The cross validation LOO and LMO were performed on the resulted MLR models, the models 13 - 17 showed a good predictive power. The PCA was performed to divide the data into three data sets; training, validation and test set. Then the ANN performed on the choosed models 13-17. The resulted ANN models were validated by randomization test, then the conditions that proposed by Golbraikh and Tropsha were applied to confirm that the QSAR models have acceptable prediction power or not. However the best ANN model with the best predictive power was model number 17, with R test value 0.8138. A new suggested compounds with IC50 7.057 and 3.336 μg/ml. From the above result, now it’s possible to design a new potent antimalarial drug by the application of the best model equation of MLR.