Literature DB >> 24661111

EGFRIndb: epidermal growth factor receptor inhibitor database.

Inderjit S Yadav, Harinder Singh, Mohd Imran Khan, Ashok Chaudhury, G P S Raghava, Subhash M Agarwal1.   

Abstract

BACKGROUND: Aberrant activity of epidermal growth factor receptor (EGFR) family proteins has been found to be associated with a number of human cancers including that of lung and breast. Consequently, the search for EGFR family inhibitors, a well established target of pharmacological and therapeutic value has been ongoing. Therefore, over the years several small molecules, which compete for ATP in the kinase domain have been synthesised and some of them have proved to be effective in attenuating EGFR mediated proliferation. Thus, there exists in literature a vast amount of experimental data on EGFR tyrosine kinase inhibitors. In this paper, we describe a comprehensive database EGFRIndb that contains details of the small molecular inhibitors of EGFR family. DESCRIPTION: EGFRIndb is a literature curated database of small synthetic molecular inhibitors of EGFR. It consists of 4581 compounds showing in vitro inhibitory activities (IC50, IC80, GI50, GI90, EC50, Ki, Kd and percentage inhibition) either against EGFR or its different isoforms i.e. Erbb2 (v-erb-b2 avian erythroblastic leukaemia viral oncogene homolog 2) and Erbb4 (v-erb-b2 avian erythroblastic leukaemia viral oncogene homolog 4) or various mutants. For each compound, database provides information on structure, experimentally determined inhibitory activity of compound against kinase as well as various cell lines, properties (physical, elemental and topological) and drug likeness. Additionally, it provides information on irreversible as well as dual inhibitors that have gained importance in recent years due to the emergence of clinical resistance to known drugs. As compound activity against similar kinases is a measure of its selectivity and specificity, the database also provides this information. It also provides simple search, advanced search, browse facility as well as a tool for structure based searching.
CONCLUSION: EGFRIndb gathers biological and chemical information on EGFR inhibitors from the literature. It is hoped that it will serve as a useful resource in drug discovery and provide data for docking, virtual screening and Quantitative structure-activity relationship (QSAR) model development to the cancer researchers.

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Year:  2014        PMID: 24661111     DOI: 10.2174/1871520614666140323203140

Source DB:  PubMed          Journal:  Anticancer Agents Med Chem        ISSN: 1871-5206            Impact factor:   2.505


  7 in total

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2.  Computational identification of natural product inhibitors against EGFR double mutant (T790M/L858R) by integrating ADMET, machine learning, molecular docking and a dynamics approach.

Authors:  Subhash M Agarwal; Prajwal Nandekar; Ravi Saini
Journal:  RSC Adv       Date:  2022-06-07       Impact factor: 4.036

3.  EGFR Mutant Structural Database: computationally predicted 3D structures and the corresponding binding free energies with gefitinib and erlotinib.

Authors:  Lichun Ma; Debby D Wang; Yiqing Huang; Hong Yan; Maria P Wong; Victor H F Lee
Journal:  BMC Bioinformatics       Date:  2015-03-14       Impact factor: 3.169

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

Authors:  Harinder Singh; Sandeep Singh; Deepak Singla; Subhash M Agarwal; Gajendra P S Raghava
Journal:  Biol Direct       Date:  2015-03-25       Impact factor: 4.540

5.  A web server for analysis, comparison and prediction of protein ligand binding sites.

Authors:  Harinder Singh; Hemant Kumar Srivastava; Gajendra P S Raghava
Journal:  Biol Direct       Date:  2016-03-25       Impact factor: 4.540

6.  Prediction of anticancer molecules using hybrid model developed on molecules screened against NCI-60 cancer cell lines.

Authors:  Harinder Singh; Rahul Kumar; Sandeep Singh; Kumardeep Chaudhary; Ankur Gautam; Gajendra P S Raghava
Journal:  BMC Cancer       Date:  2016-02-09       Impact factor: 4.430

7.  EGFRisopred: a machine learning-based classification model for identifying isoform-specific inhibitors against EGFR and HER2.

Authors:  Ravi Saini; Subhash Mohan Agarwal
Journal:  Mol Divers       Date:  2021-08-03       Impact factor: 2.943

  7 in total

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