Exploration of QSAR modelling techniques and their combination to rationalize the physicochemical characters of nitrophenyl derivatives towards aldose reductase inhibition.
The quantitative structure-activity relationship (QSAR) analysis of nitrophenyls active as aldose reductase inhibitors (ARIs) has been performed employing Fujita-Ban, classical Hansch approach and physicochemical properties. The multivariant regression expressions were developed using sequential multiple linear regression (SEQ-MLR) techniques considering adjustable correlation coefficient (r(2)(adj)). The statistical quality of SEQ-MLR equations were evaluated considering parameters like correlation coefficient (r), standard error of estimation (SEE), and variance ratio (F) at explicit degree of freedom (df). Orthogonality of the descriptors in SEQ-MLR was established through variance inflation factor (VIF).The QSAR analysis gave insight to some common important structural feature i.e. the hydroxyl group is crucial for hydrogen bond interaction with the receptor. Similarly electro-negative substitution is essential for polar interaction with charge region of the receptor. Moreover analysis inferred that diarylsulphides can be explored for optimization of the analogs.
Soni
Love Kumar
LK
Molecular Modelling Study Group CADD Laboratory, Computational Chemistry Research Department of Pharmacy, Shri GS Institute of Technology & Science, Indore 452 003, India. lovesoni@hotmail.com
Gupta
Arun Kumar
AK
Kaskhedikar
S G
SG
D000449:Aldehyde Reductase / Q000037:antagonists & inhibitors*; D004789:Enzyme Activation / Q000187:drug effects; D004791:Enzyme Inhibitors / Q000737:chemistry* / Q000494:pharmacology*; D008958:Models, Molecular; D015394:Molecular Structure; D009578:Nitrobenzenes / Q000737:chemistry* / Q000494:pharmacology*; D021281:Quantitative Structure-Activity Relationship
D004791:Enzyme Inhibitors; D009578:Nitrobenzenes; D000449:Aldehyde Reductase