QSAR Investigation on Quinolizidinyl Derivatives in Alzheimer's Disease. (30th April 2013)

• Record Type: Journal Article
• Authors: Ghasemi, Ghasem; Arshadi, Sattar; Rashtehroodi, Alireza Nemati; Nirouei, Mahyar; Shariati, Shahab; Rastgoo, Zinab
• Other Names: Ng Hon Keung Tony Academic Editor.
• Is Part Of: Journal of computational medicine. Volume 2013(2013)
• Publication Date: 2013-04-30
• Subjects: Medical informatics -- Periodicals; Computational intelligence -- Periodicals; Computational intelligence; Medical informatics; Periodicals; Electronic journals; 610.285
• DOI: 10.1155/2013/312728
• Languages: English
• ISSNs: 2314-5080

Abstract

Abstract : 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 R 2 using MLR method were obtained as 0.1434 and 0.95, respectively. Also, the R and R 2 values were obtained as 0.79, 0.62 from stepwise MLR model. The R 2 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 R 2 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.