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Quantitative Structure-Activity Relationship Studies on 2-Amino-6-Arylsulfonylbenzonitriles As Human Immunodeficiency Viruses Type 1 Reverse Transcriptase Inhibitors Using Descriptors Obtained From Substituents and Whole Molecular Structures Publisher Pubmed



Hemmateenejad B1, 2 ; Sabet R2, 3, 4 ; Fassihi A3, 4
Authors
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Authors Affiliations
  1. 1. Department of Chemistry, Shiraz University, 71454 Shiraz, Iran
  2. 2. Medicinal and Natural Products Chemistry Research Center, Shiraz University of Medical Sciences, 71454 Shiraz, Iran
  3. 3. Department of Medicinal Chemistry, Faculty of Pharmacy, Isfahan University of Medical Sciences, 81746-73461 Isfahan, Iran
  4. 4. Isfahan Pharmaceutical Sciences Research Center, 81746-73461 Isfahan, Iran

Source: Chemical Biology and Drug Design Published:2009


Abstract

The human immunodeficiency viruses type 1 reverse transcriptase is a major target for drug development. Inhibition of this enzyme has been one of the primary therapeutic strategies in suppressing the replication of human immunodeficiency viruses type 1. A series of 2-amino-6-arylsulfonylbenzonitrile derivatives was subjected to quantitative structure-activity relationship analysis. The newly proposed substituent electronic descriptors were investigated for quantitative structure-activity relationship modeling of the compounds and a comparison was made with the conventional molecular descriptors. Two chemometrics methods including multiple linear regressions and partial least squares combined with genetic algorithm for variable selection were employed to make connections between structural parameters and enzyme inhibition. The results revealed the significant roles of topological, geometrical and substituent electronic descriptor parameters on the human immunodeficiency viruses type 1 reverse transcriptase inhibitory activity of the studied molecules. The selected substituent electronic descriptor parameters revealed that more electronegative and less polar substituents as meta position and more electrophile substituents as para positions are favorable for higher activity. It was found that electronic descriptors calculated for substituents (substituent electronic descriptor parameters) could explain 80% of variances in the biological activity data. The most significant quantitative structure-activity relationship model, obtained by partial least squares combined with genetic algorithm, could explain and predict 90% and 85% of variances in the pIC50 data, respectively. © 2009 John Wiley & Sons A/S.
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