TY  - JOUR
N1  - This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

ID  - open1638
UR  - http://www.biologydirect.com/content/10/1/10
A1  - Singh, Harinder
A1  - Singh, Sandeep
A1  - Singla, Deepak
A1  - Agarwal, Subhash M
A1  - Raghava, G.P.S.
Y1  - 2015///
N2  - Epidermal Growth Factor Receptor (EGFR) is a well-characterized cancer drug target. In the past, several QSAR models have been developed for predicting inhibition activity of molecules against EGFR. These models are useful to a limited set of molecules for a particular class like quinazoline-derivatives. In this study, an attempt has been made to develop prediction models on a large set of molecules (~3500 molecules) that include diverse scaffolds like quinazoline, pyrimidine, quinoline and indole.
PB  - BioMedCentral
JF  - Biology direct
VL  - 10
KW  -  EGFR inhibitors; Classification of EGFR inhibitors and non-inhibitors; Active substructure; Active functional groups; PubChem fingerprint; QSAR; Random forest
SN  - 1745-6150
TI  - QSAR based model for discriminating EGFR inhibitors and non-inhibitors using Random forest.
ER  -