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In Silico Multivariate Regressio n Analysis and Validation Studies on Selective MMP-13 Inhibitors

by G. Nirmala, A. Yesubabu, P. Seetharamaiah
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 130 - Number 6
Year of Publication: 2015
Authors: G. Nirmala, A. Yesubabu, P. Seetharamaiah
10.5120/ijca2015907019

G. Nirmala, A. Yesubabu, P. Seetharamaiah . In Silico Multivariate Regressio n Analysis and Validation Studies on Selective MMP-13 Inhibitors. International Journal of Computer Applications. 130, 6 ( November 2015), 24-37. DOI=10.5120/ijca2015907019

@article{ 10.5120/ijca2015907019,
author = { G. Nirmala, A. Yesubabu, P. Seetharamaiah },
title = { In Silico Multivariate Regressio n Analysis and Validation Studies on Selective MMP-13 Inhibitors },
journal = { International Journal of Computer Applications },
issue_date = { November 2015 },
volume = { 130 },
number = { 6 },
month = { November },
year = { 2015 },
issn = { 0975-8887 },
pages = { 24-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume130/number6/23213-2015907019/ },
doi = { 10.5120/ijca2015907019 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:24:38.446994+05:30
%A G. Nirmala
%A A. Yesubabu
%A P. Seetharamaiah
%T In Silico Multivariate Regressio n Analysis and Validation Studies on Selective MMP-13 Inhibitors
%J International Journal of Computer Applications
%@ 0975-8887
%V 130
%N 6
%P 24-37
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

QSAR(Quantitative Structure Activity Relationship) studies were carried out on a set of 72 α-sulfone hydroxamatesas Matrix Metalloproteinase-13 (MMP-13) inhibitors using multiple regression procedure. Outliers were removed based on Relative Error calculation and Extent of Extrapolation. The activity contributions of these compounds were determined from regression equation and the validation procedures such as external set cross-validation r2, (R2cv, ext) and the regression of observed activities versus predicted activities and vice versa for validation set was described to analyze the predictive ability of the QSAR model. Parameters concerning predictive ability of QSAR model and Y-randomization tests were found to be within the limits. From a set of 5 models, an accurate and reliable QSAR model involving six descriptors was chosen based on the FIT Kubinyi function, which defines the statistical quality of the model. The generated model could be useful in designing more potent inhibitors of MMP-13.

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Index Terms

Computer Science
Information Sciences

Keywords

α-sulfone hydroxamates QSAR Multiple regression Cross validation Outliers FIT Kubinyi descriptors MMP-13.

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