Title:Molecular Modelling Studies on Thiazole-Based α-Glucosidase Inhibitors Using Docking and CoMFA, CoMSIA and HQSAR
Volume: 18
Issue: 6
Author(s): Deshbandhu Joshi*, Shourya Yadav, Rajesh Sharma, Mitrunjaya Pandya and Raghvendra Singh Bhadauria
Affiliation:
- School of Pharmacy, Devi Ahilya Vishwavidyalya Takshashila Campus, Khandwa Road, Indore-452001 Madhya Pradesh,India
Keywords:
CoMFA, CoMSIA, HQSAR, pharmacophore mapping, docking, α-glucosidase inhibitor.
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.
