| ORIGINAL ARTICLE |
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| Year : 2015 | Volume
: 6
| Issue : 1 | Page : 13-18 |
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Three-dimensional quantitative structure-activity relationships and docking studies of some structurally diverse flavonoids and design of new aldose reductase inhibitors
Utpal Chandra De1, Tanusree Debnath2, Debanjan Sen3, Sudhan Debnath2
1 Department of Chemistry, Tripura University, Agartala, Tripura, India
2 Department of Chemistry, MBB College, Agartala, Tripura, India
3 Department of Pharmacy, Bengal Institute of Pharmaceutical Sciences, Kalyani, Nadia, West Bengal, India
Correspondence Address:
Sudhan Debnath
Department of Chemistry, MBB College, West Tripura, Agartala, Tripura - 799 004
India
 Source of Support: Financial Support from DBT, New Delhi., Conflict of Interest: None  | Check |
DOI: 10.4103/2231-4040.150366
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Aldose reductase (AR) plays an important role in the development of several long-term diabetic complications. Inhibition of AR activities is a strategy for controlling complications arising from chronic diabetes. Several AR inhibitors have been reported in the literature. Flavonoid type compounds are shown to have significant AR inhibition. The objective of this study was to perform a computational work to get an idea about structural insight of flavonoid type compounds for developing as well as for searching new flavonoid based AR inhibitors. The data-set comprising 68 flavones along with their pIC 50 values ranging from 0.44 to 4.59 have been collected from literature. Structure of all the flavonoids were drawn in Chembiodraw Ultra 11.0, converted into corresponding three-dimensional structure, saved as mole file and then imported to maestro project table. Imported ligands were prepared using LigPrep option of maestro 9.6 version. Three-dimensional quantitative structure-activity relationships and docking studies were performed with appropriate options of maestro 9.6 version installed in HP Z820 workstation with CentOS 6.3 (Linux). A model with partial least squares factor 5, standard deviation 0.2482, R 2 = 0.9502 and variance ratio of regression 122 has been found as the best statistical model. |
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