Journal of Computational Medicine
Volume 2013 (2013), Article ID 312728, 8 pages
QSAR Investigation on Quinolizidinyl Derivatives in Alzheimer’s Disease
1Department of Chemistry, Rasht Branch, Islamic Azad University, Rasht, Iran
2Department of Chemistry, Payame Noor University, Behshahr Branch, Behshahr, Iran
3Department of Chemistry, Payame Noor University, Sari Branch, Sari, Iran
4Department of Electrical Engineering, Lahijan Branch, Islamic Azad University, Lahijan, Iran
Received 15 December 2012; Revised 24 March 2013; Accepted 7 April 2013
Academic Editor: Hon Keung Tony Ng
Copyright © 2013 Ghasem Ghasemi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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 and R2 using MLR method were obtained as 0.1434 and 0.95, respectively. Also, the R and R2 values were obtained as 0.79, 0.62 from stepwise MLR model. The R2 and mean square values using LASSO method were obtained as 0.766 and 3.226, respectively. The root mean square error of the calibration and R2 using 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.