Deshbandhu Joshi*, Shourya Yadav, Rajesh Sharma, Mitrunjaya Pandya and Raghvendra Singh Bhadauria Pages 1 - 16 ( 16 )
Aims and Objective: 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 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 created utilizing 37 as the training set and externally validated utilizing 9 as a test set. The CoMFA models MMFF94 were generated and ranging from 3.4661 to 5.2749 using leave-one-out PLS analysis cross-validated correlation coefficient q2 0.787 a high non-crossvalidated 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, r 2 0.833 was attained, (SEE) 0.065, F value 520.302 and r2pred 0.990. Contribution of steric, electrostatic, hydrophobic, donor and acceptor fields were 0.151, 0.268, 0.223, 0.234, 0.124 respectively.
Conclusion: The HQSAR model of training set exhibits significant cross-validated correlation coefficient q2 0.800 and noncross-validated correlation coefficient r2 0.943.
CoMFA, CoMSIA, HQSAR, pharmacophore mapping, docking, α-glucosidase inhibitor
School of Pharmacy, Devi AhilyaVishwavidyalyaTakshashila Campus, Khandwa Road, Indore -452001 Madhya Pradesh, Shrinathji Insitutte of Pharmacy, Upali Oden, Nathdwara, Rajsmand-313301 Rajasthan, School of Pharmacy, Devi AhilyaVishwavidyalyaTakshashila Campus, Khandwa Road, Indore -452001 Madhya Pradesh, School of Pharmacy, Devi AhilyaVishwavidyalyaTakshashila Campus, Khandwa Road, Indore -452001 Madhya Pradesh, Shrinathji Insitutte of Pharmacy, Upali Oden, Nathdwara, Rajsmand-313301 Rajasthan