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

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.