Letters in Drug Design & Discovery

G. Perry
University of Texas
San Antonio, TX
USA
Email: lddd@benthamscience.org

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2D QSAR Studies on a Series of Quinazoline Derivatives as Tyrosine Kinase (EGFR) Inhibitor: An Approach to Design Anticancer Agents

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

Abstract:

Epidermal growth factor receptor (EGFR) protein tyrosine kinases (PTKs) are known for its role in cancer. QSAR studies were performed on a set of 137 analogs of quinazoline using MDS vlife science QSAR plus module by using Multiple Linear Regression (MLR), Principal Component Regression (PCR) and Partial Least Squares (PLS) Regression methods. Among these, MLR method has shown a very promising result as compared to other two methods and a QSAR model was generated by a training set of 100 molecules with correlation coefficient (r2) of 0.884, significant cross validated correlation coefficient (q2) of 0.800, F test of 39.5149, r2 for external test set (pred_r2) 0.5902, coefficient of correlation of predicted data set (pred_r2se) 0.7438 and degree of freedom 83. In the selected descriptors, alignment independent descriptors T_2_C_5 (11.74%) and T_2_O_7 (-11.27%) are the most important descriptors in predicting EGFR inhibitory activity. Electron withdrawing group at anilino quinazolines enhances the activity as evident by positive value of T_F_Cl_4 (2.07%). In addition, for quinazoline substituents, estate contribution descriptors, SaaCHE – index and Ssss NE-index have a large deactivating effect.

Keywords: Quinazoline, Tyrosine kinase (EGFR), Multiple Linear Regression (MLR), Principal Component Regression (PCR), Partial Least Squares (PLS)

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Article Details

VOLUME: 7
ISSUE: 8
Year: 2010
Page: [556 - 586]
Pages: 31
DOI: 10.2174/157018010792062821
Price: $58