Generic placeholder image

Letters in Drug Design & Discovery

Editor-in-Chief

ISSN (Print): 1570-1808
ISSN (Online): 1875-628X

A QSAR Analysis of Coumarin Derivatives as TNF-α Inhibitor - A Rational Approach to Anticancer Drug Design

Author(s): Malleshappa N. Noolvi, Harun M. Patel and Tarandeep Kaur

Volume 8, Issue 9, 2011

Page: [868 - 876] Pages: 9

DOI: 10.2174/157018011797200768

Price: $65

Abstract

A set of one hundred and twenty two coumarin derivatives with TNF-α inhibitory activity was subjected to the two dimensional quantitative structure activity relationships (2D-QSAR) studies using MDS 3.0 drug designing module with Multiple Linear Regression (MLR), Principle Component Regression (PCR) and Partial Least Square Regression (PLS) analysis being carried out. Among these three methods, PCR-model has come out with significant result as compared to other models. The best PCR QSAR model (r2 = 0.8721, Fisher test value F = 40.67, Pred_r2 = 0.654) has acceptable statistical quality and predictive potential as indicated by the value of cross validated squared correlation coefficient (q2= 0.7123). From the build model it seems to be clear that SaaCHE-index, T_2_O_1, T_N_O_6 contributes positively while as SssCH2E-index descriptor negatively contributes to the biological activity. Thus this validated model brings important structural insight to aid the design of novel coumarins TNF- α inhibitor as anti-cancer agents.

Keywords: Coumarins, Cancer, TNF-α, Multiple Linear Regression (MLR), Principle Component Regression (PCR), Partial Least Square Regression (PLSR), QSAR, 1H NMR, Mass spectroscopical data, cancer, molecular anatomy, benzothiazole, aryl hydrocarbon receptor (AhR), melanoma, leismaniacidal, orthophosphoric acid


Rights & Permissions Print Export Cite as
© 2024 Bentham Science Publishers | Privacy Policy