QSAR based model for discriminating EGFR inhibitors and non-inhibitors using Random forest.

Singh, Harinder and Singh, Sandeep and Singla, Deepak and Agarwal, Subhash M and Raghava, G.P.S. (2015) QSAR based model for discriminating EGFR inhibitors and non-inhibitors using Random forest. Biology direct, 10. p. 10. ISSN 1745-6150

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Abstract

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.

Item Type:	Article
Additional Information:	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.
Uncontrolled Keywords:	EGFR inhibitors; Classification of EGFR inhibitors and non-inhibitors; Active substructure; Active functional groups; PubChem fingerprint; QSAR; Random forest
Subjects:	Q Science > QR Microbiology
Depositing User:	Dr. K.P.S.Sengar
Date Deposited:	13 Jul 2015 12:43
Last Modified:	14 Jul 2015 05:10
URI:	http://crdd.osdd.net/open/id/eprint/1638

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