alexa Abstract | Cluster and multi-linear regression analyses guided identification of molecular descriptors that account for cyclooxygenase activities

Journal of Chemical and Pharmaceutical Research
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Cluster analysis and multiple linear regression analysis were employed to determine the distinct group of molecular descriptors that largely account for the biological activity of known inhibitors against cyclooxygenase (COX). In 157 out of 3227 molecular descriptors the nonselective COX inhibitors and COX-2 selective inhibitors form two distinct clusters. Multiple linear regression analysis returned three equations accounting for the pIC50 of the inhibitors against COX. For the pIC50 of nonselective COX inhibitors against COX-1, the molecular descriptors with the highest importance include GGI1, GGI10, SM1_Dzm and Eta_alpha_A. For the pIC50 of nonselective COX inhibitors against COX-2, molecular descriptors Ts, GGI1, SpMax3_Bhv and GGI10 were key to the observed activity. The observed variation in pIC50 of COX-2selective inhibitors against COX-2 were attributed toSpMax3_Bhp, SpMax_AEAdm, VE2_Be, SM5_L, Eta_betaS, G2, Eig04_EAed, H_DzZ, SM4_L and VE3_Bp. The results of the study can be used to understand the nature of COX inhibitors and to further facilitate the development of COX-2 selective inhibitors.

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Author(s): Viktoria Shade L Vios and Junie B Billones


COX inhibitors, COX-1, COX-2, Cluster Analysis, Multiple Linear Regression Analysis, QSAR, NSAID, DRAGON® Descriptors, cyclooxygenase activities

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