Quantitative structure-activity relationship (QSAR) modelling of N-aryl derivatives as cholinesterase inhibitors

• Published in: 2012 IEEE Symposium on Humanities, Science and Engineering Research pp. 907 - 912
• Main Authors: Ridzuan, M. S. M., Jaafar, M. Z., Zain, M. M.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2012
• Subjects: Alzheimer's disease; Biological system modeling; Compounds; Correlation; Drugs; Mathematical model; Multiple Linear Regression (MLR); N-aryl derivatives; Predictive models; QSAR; Software
• ISBN: 9781467313117; 1467313114
• ISSN: 2378-9808
• DOI: 10.1109/SHUSER.2012.6269006

A QSAR study on a series of N-aryl derivatives was performed to explore the important molecular descriptor which is responsible for their inhibitory activity towards choli nest erase enzyme, the common target for the treatment of Alzheimer's disease. Molecular descriptors were calculated using DRAGON version 5.2 software Two methods of descriptor selection, stepwise regression and forward selection procedure, were performed and compared. Multiple Linear Regression (MLR) analysis was carried out to derive QSAR models, which were further evaluated for statistical significance and predictive power by leave-one-out (LOO) cross validation test. The best QSAR models against acetylcholinesterase and butylcholinesterase inhibitory activity were selected, having squared correlation coefficient R 2 =945% and 98.4%, and cross-validated squared correlation coefficient R 2 cv = 91.9% and 97.2%, respectively. The statistical outcomes derived from the present study demonstrate good predictability and may be useful in the design of more potent substituted N-aryl derivatives as cholinesterase inhibitor.