References
- Wieduwilt MJ, Moasser MM. The epidermal growth factor receptor family: biology driving targeted therapeutics. Cell Mol Life Sci. 2008;65:1566–1584.
- Maruyama IN. Mechanisms of activation of receptor tyrosine kinases: monomers or dimers. Cells 2014; 3:304–330.
- Schlessinger J. Cell signaling by receptor tyrosine kinases. Cell 2000;103:211–225.
- Lemmon MA, Schlessinger J. Cell signaling by receptor tyrosine kinases. Cell 2010;141:1117–1134.
- Guo G, Gong K, Wohlfeld B, et al. Ligand-independent EGFR signaling. Cancer Res. 2015;75:3436–3441.
- Hackel BJ, Neil JR, White FM, et al. Epidermal growth factor receptor downregulation by small heterodimeric binding proteins. Protein Eng Des Sel. 2012;25:47–57.
- Yu X, Sharma KD, Takahashi T, et al. Ligand-independent dimer formation of epidermal growth factor receptor (EGFR) is a step separable from ligand-induced EGFR signaling. Mol Biol Cell. 2002;13:2547–2557.
- Bethune G, Bethune D, Ridgway N, et al. Epidermal growth factor receptor (EGFR) in lung cancer: an overview and update. J Thorac Dis. 2010;2:48–51.
- Thomas RK, Weir B, Meyerson M. Genomic approaches to lung cancer. Clin Cancer Res. 2006;12:4384s–4391s.
- Yun CH, Boggon TJ, Li Y, et al. Structures of lung cancer-derived EGFR mutants and inhibitor complexes: mechanism of activation and insights into differential inhibitor sensitivity. Cancer Cell 2007;11:217–227.
- Sato T, Watanabe H, Tsuganezawa K, et al. Identification of novel drug-resistant EGFR mutant inhibitors by in silico screening using comprehensive assessments of protein structures. Bioorg Med Chem. 2012;20:3756–3767.
- Chan BA, Hughes BG. Targeted therapy for non-small cell lung cancer: current standards and the promise of the future. Transl Lung Cancer Res. 2015;4:36–54.
- Liao BC, Lin CC, Yang JC. Second and third-generation epidermal growth factor receptor tyrosine kinase inhibitors in advanced nonsmall cell lung cancer. Curr Opin Oncol. 2015;2:94–101.
- Sullivan I, Planchard D. Next-generation EGFR tyrosine kinase inhibitors for treating EGFR-mutant lung cancer beyond first line. Front Med (Lausanne). 2017;3:76.
- Kosaka T, Yatabe Y, Endoh H, et al. Analysis of epidermal growth factor receptor gene mutation in patients with non-small cell lung cancer and acquired resistance to gefitinib. Clin Cancer Res. 2006;12:5764–5769.
- Kobayashi S, Boggon TJ, Dayaram T, et al. EGFR mutation and resistance of non-small-cell lung cancer to gefitinib. N Engl J Med. 2005;352:786–792.
- Pao W, Miller VA, Politi KA, et al. Acquired resistance of lung adenocarcinomas to gefitinib or erlotinib is associated with a second mutation in the EGFR kinase domain. PLoS Med. 2005;2:e73.
- Gajiwala KS, Feng J, Ferre R, et al. Insights into the aberrant activity of mutant EGFR kinase domain and drug recognition. Structure. 2013;21:209–219.
- Hung CL, Chen CC. Computational approaches for drug discovery. Drug Dev Res. 2014;75:412–418.
- Sliwoski G, Kothiwale S, Meiler J, et al. Computational methods in drug discovery. Pharmacol Rev. 2013;66:334–395.
- Szklarczyk D, Santos A, von Mering C, et al. STITCH 5: augmenting protein-chemical interaction networks with tissue and affinity data. Nucleic Acids Res. 2016;44:D380–D384.
- Kim S, Han L, Yu B, et al. PubChem structure-activity relationship (SAR) clusters. J Cheminform. 2015;7:33.
- Wang Y, Bryant SH, Cheng T, et al. PubChem BioAssay: 2017 update. Nucleic Acids Res. 2017;45:D955–D963.
- Pradeepkiran JA, Kumar KK, Kumar YN, et al. Modeling, molecular dynamics, and docking assessment of transcription factor rho: a potential drug target in Brucella melitensis 16M. Drug Des Devel Ther. 2015;9:1897–1912.
- Volkamer A, Kuhn D, Rippmann F, et al. DoGSiteScorer: a web server for automatic binding site prediction, analysis and druggability assessment. Bioinformatics. 2012;28:2074–2075.
- Dallakyan S, Olson AJ. Small-molecule library screening by docking with PyRx. Methods Mol Biol. 2015;1263:243–250.
- Wolber G, Langer T. LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. J Chem Inf Model. 2005;45:160–169.
- Singh H, Singh S, Singla D, et al. QSAR based model for discriminating EGFR inhibitors and non-inhibitors using Random forest. Biol Direct. 2015;10:10.
- Singh H, Kumar R, Singh S, et al. Prediction of anticancer molecules using hybrid model developed on molecules screened against NCI-60 cancer cell lines. BMC Cancer. 2016;16:77.
- Daina A, Michielin O, Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep. 2017;7:42717.
- Cheng F, Li W, Zhou Y, et al. admetSAR: a comprehensive source and free tool for assessment of chemical ADMET properties. J Chem Inf Model. 2012; 52:3099–3105.
- Pereira M, Verma CS, Fuentes G. Differences in the binding affinities of ErbB family: heterogeneity in the prediction of resistance mutants. PLoS One. 2013; 8:e77054
- Dougherty DA. Cation-pi interactions in chemistry and biology: a new view of benzene, Phe, Tyr, and Trp. Science. 1996; 271:163–168.
- Gallivan JP, Dougherty DA. Cation-pi interactions in structural biology. Proc Natl Acad Sci USA. 1999;96:9459–9464.
- Lipinski CA, Lombardo F, Dominy BW, et al. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev. 2001;46:3–26.