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Exploring QSAR for Substituted 2‐Sulfonyl‐Phenyl‐Indol Derivatives as Potent and Selective COX‐2 Inhibitors Using Different Chemometrics Tools
Author(s) -
Khoshneviszadeh Mehdi,
Edraki Najmeh,
Miri Ramin,
Hemmateenejad Bahram
Publication year - 2008
Publication title -
chemical biology and drug design
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.59
H-Index - 77
eISSN - 1747-0285
pISSN - 1747-0277
DOI - 10.1111/j.1747-0285.2008.00735.x
Subject(s) - partial least squares regression , quantitative structure–activity relationship , principal component analysis , chemometrics , linear regression , principal component regression , chemistry , regression analysis , lipophilicity , stepwise regression , biological system , mathematics , stereochemistry , statistics , chromatography , biology
Selective inhibition of cyclooxygenase‐2 inhibitors is an important strategy in designing of potent anti‐inflammatory compounds with significantly reduced side effects. The present quantitative structure–activity relationship study, attempts to explore the structural and physicochemical requirements of 2‐sulfonyl–phenyl–indol derivatives ( n = 30) for COX‐2 inhibitory activity using chemical, topological, geometrical, and quantum descriptors. Some statistical techniques like stepwise regression, multiple linear regression with factor analysis as the data preprocessing (FA‐MLR), principal component regression analysis, and genetic algorithms partial least squares analysis were applied to derive the quantitative structure–activity relationship models. The generated equations were statistically validated using cross‐validation and external test set. The quality of equations obtained from stepwise multiple linear regression, FA‐MLR, principal component regression analysis and PLS were in the acceptable statistical range. The best multiple linear regression equation obtained from factor analysis (FA‐MLR) as the preprocessing step could predict 77.5% of the variance of the cyclooxygenase‐2 inhibitory activity whereas that derived from genetic algorithms partial least squares could predict 84.2% of variances. The results of quantitative structure–activity relationship models suggested the importance of lipophilicity, electronegativity, molecular area and steric parameters on the cyclooxygenase‐2 inhibitory activity.