@article{open1638,
          volume = {10},
          author = {Harinder Singh and Sandeep Singh and Deepak Singla and Subhash M Agarwal and G.P.S. Raghava},
            note = {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.
},
           title = {QSAR based model for discriminating EGFR inhibitors and non-inhibitors using Random forest.},
       publisher = {BioMedCentral},
         journal = {Biology direct},
           pages = {10},
            year = {2015},
        keywords = { EGFR inhibitors; Classification of EGFR inhibitors and non-inhibitors; Active substructure; Active functional groups; PubChem fingerprint; QSAR; Random forest},
             url = {http://crdd.osdd.net/open/1638/},
        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 ({\texttt{\char126}}3500 molecules) that include diverse scaffolds like quinazoline, pyrimidine, quinoline and indole.}
}