Structural analysis of structurally diverse alpha-glucosidase inhibitors for active site feature analysis

• Type: Article in International Scientific Journal
• Date: 2012

Authors

Hari Narayana Moorthy, NSHN (Author) Other; Maria J Ramos (Author) FCUP; Pedro A Fernandes (Author) FCUP

Journal

Title: Journal of Enzyme Inhibition and Medicinal Chemistry

Vol. 27

Pages: 649-657

ISSN: 1475-6366

Publisher: Taylor & Francis

Indexing

ISI Web of Knowledge - 7 Citations

Scopus - 7 Citations

Scientific classification

FOS: Medical and Health sciences > Basic medicine

Other information

Authenticus ID: P-002-57N

DOI: 10.3109/14756366.2011.605359

Abstract (EN): In the present investigation, a QSAR analysis on structurally diverse alpha-glucosidase inhibitors (andrographolide, chromenone, triazole derivatives) was performed and the developed models were validated by various validation methods (LMO, LOO, LSO, bootstrapping, Y-randomization and test set). The statistical parameters calculated for the models show that the developed models are statistically significant and have predicted the activities with small residual errors. The crossvalidated correlation coefficient (Q(2)) values obtained from different validation methods show >0.7 for both the models. Other correlations coefficient statistical parameters (R-pred(2) and R-m(2)) show that the developed models are reliable and robust. The leave-series-out (LSO) results reveal that the developed models can predict the activity of new compounds and its crossvalidated correlation coefficients' values are comparable with the Q(2) values obtained from other validation methods. The descriptors contributed in the selected models are suggested that the lower/reduced polarizability on the vdW surface area of the molecules and the presence of flexible bonds allow the substituents/side chains in the molecules with free movement and with lesser stretching energy which are favourable for the alpha-glucosidase inhibitory activity. These results reveal that the developed models are statistically significant and can be used with other molecular modelling works for designing novel alpha-glucosidase inhibitors with multiple activities (HIV, diabetics, cancer, etc).

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 9