Generic placeholder image

Current Drug Discovery Technologies

Editor-in-Chief

ISSN (Print): 1570-1638
ISSN (Online): 1875-6220

Research Article

Molecular Modelling Studies on Thiazole-Based α-Glucosidase Inhibitors Using Docking and CoMFA, CoMSIA and HQSAR

Author(s): Deshbandhu Joshi*, Shourya Yadav, Rajesh Sharma, Mitrunjaya Pandya and Raghvendra Singh Bhadauria

Volume 18, Issue 6, 2021

Published on: 22 October, 2020

Article ID: e130921187100 Pages: 16

DOI: 10.2174/1570163817666201022111213

Price: $65

Abstract

Aims and Objectives: The biological dataset was retrieved from two series of α-glucosidase inhibitors synthesized by Rahim et al. and Taha et al. and consisted of a total of 46 (forty-six) α- glucosidase inhibitors.

Methods: The α-glucosidase inhibitory IC50 values (μM; performed against α-glucosidase from Saccharomyces cerevisiae) were converted into negative logarithmic units (pIC50). The CoMFA and CoMSIA models were developed using 37 as a training set, and externally validated using 9 as a test set. The CoMFA models MMFF94 were generated, ranging from 3.4661 to 5.2749 using leave-oneout PLS analysis cross-validated correlation coefficient q2 0.787, a high non-cross-validated correlation coefficient r2 0.819, with a low Standard Error Estimation (SEE) 0.041, F value 1316.074 and r2pred 0.996.

Results: The steric and electrostatic fields contributions were 0.507 and 0.493, respectively. The CoMSIA model q2 0.805, r2 0.833 was attained, (SEE) 0.065, F value 520.302 and r2pred 0.990. Contribution of steric, electrostatic, hydrophobic, donor and acceptor fields was 0.151, 0.268, 0.223, 0.234, 0.124, respectively.

Conclusion: The HQSAR model of the training set exhibits a significant cross-validated correlation coefficient q2 0.800 and non-cross-validated correlation coefficient r2 0.943.

Keywords: CoMFA, CoMSIA, HQSAR, pharmacophore mapping, docking, α-glucosidase inhibitor.

Graphical Abstract

Rights & Permissions Print Export Cite as
© 2022 Bentham Science Publishers | Privacy Policy