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

DIR@IMTECH: CSIR-Institute of Microbial Technology

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Title QSAR based model for discriminating EGFR inhibitors and non-inhibitors using Random forest.
 
Creator Singh, Harinder
Singh, Sandeep
Singla, Deepak
Agarwal, Subhash M
Raghava, G.P.S.
 
Subject QR Microbiology
 
Description 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.
 
Publisher BioMedCentral
 
Date 2015
 
Type Article
PeerReviewed
 
Format application/pdf
 
Identifier http://crdd.osdd.net/open/1638/1/GPSR%202015%20prot%20str%20functio%20...24783.pdf
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
 
Relation http://www.biologydirect.com/content/10/1/10
http://crdd.osdd.net/open/1638/