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3D‐Quantitative Structure–Activity Relationship Studies on Benzothiadiazepine Hydroxamates as Inhibitors of Tumor Necrosis Factor‐α Converting Enzyme
Author(s) -
Murumkar Prashant R.,
Giridhar Rajani,
Yadav Mange Ram
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.1747-0285.2008.00639.x
Subject(s) - similarity (geometry) , quantitative structure–activity relationship , outlier , test set , training set , centroid , molecular descriptor , chemistry , computational biology , mathematics , stereochemistry , artificial intelligence , computer science , statistics , biology , image (mathematics)
A set of 29 benzothiadiazepine hydroxamates having selective tumor necrosis factor‐α converting enzyme inhibitory activity were used to compare the quality and predictive power of 3D‐quantitative structure–activity relationship, comparative molecular field analysis, and comparative molecular similarity indices models for the atom‐based, centroid/atom‐based, data‐based, and docked conformer‐based alignment. Removal of two outliers from the initial training set of molecules improved the predictivity of models. Among the 3D‐quantitative structure–activity relationship models developed using the above four alignments, the database alignment provided the optimal predictive comparative molecular field analysis model for the training set with cross‐validated r 2 ( q 2 ) = 0.510, non‐cross‐validated r 2 = 0.972, standard error of estimates (s) = 0.098, and F = 215.44 and the optimal comparative molecular similarity indices model with cross‐validated r 2 ( q 2 ) = 0.556, non‐cross‐validated r 2 = 0.946, standard error of estimates (s) = 0.163, and F = 99.785. These models also showed the best test set prediction for six compounds with predictive r 2 values of 0.460 and 0.535, respectively. The contour maps obtained from 3D‐quantitative structure–activity relationship studies were appraised for activity trends for the molecules analyzed. The comparative molecular similarity indices models exhibited good external predictivity as compared with that of comparative molecular field analysis models. The data generated from the present study helped us to further design and report some novel and potent tumor necrosis factor‐α converting enzyme inhibitors.