QSAR Investigation on Quinolizidinyl Derivatives in Alzheimer’s Disease
Ghasem Ghasemi, Sattar Arshadi, Alireza Nemati Rashtehroodi, Mahyar Nirouei, Shahab Shariati, Zinab Rastgoo · Journal of Computational Medicine · 2013
Sets of quinolizidinyl derivatives of bi- and tri-cyclic (hetero) aromatic systems were studied as selective inhibitors. On the pattern, quantitative structure-activity relationship (QSAR) study has been done on quinolizidinyl derivatives as potent inhibitors of acetylcholinesterase in alzheimer’s disease (AD). Multiple linear regression (MLR), partial least squares (PLSs), principal component regression (PCR), and least absolute shrinkage and selection operator (LASSO) were used to create QSAR models. Geometry optimization of compounds was carried out by B3LYP method employing 6–31 G basis set. HyperChem, Gaussian 98 W, and Dragon software programs were used for geometry optimization of the molecules and calculation of the quantum chemical descriptors. Finally, Unscrambler program was used for the analysis of data. In the present study, the root mean square error of the calibration andR2using MLR method were obtained as 0.1434 and 0.95, respectively. Also, theRandR2values were obtained as 0.79, 0.62 from stepwise MLR model. TheR2and mean square values using LASSO method were obtained as 0.766 and 3.226, respectively. The root mean square error of the calibration andR2using PLS method were obtained as 0.3726 and 0.62, respectively. According to the obtained results, it was found that MLR model is the most favorable method in comparison with other statistical methods and is suitable for use in QSAR models.