References
- Musumeci F, Radi M, Brullo C, Schenone S. Vascular endothelial growth factor (VEGF) receptors: drugs and new inhibitors. J Med Chem. 2012;55:10797–10822.
- Ferrara N. Vascular endothelial growth factor as a target for anticancer therapy. Oncologist. 2004;9(suppl_1):2–10.
- Zhang L, Qu Y, Yang C, et al. Signaling pathway involved in hypoxia-inducible factor-1α regulation in hypoxic-ischemic cortical neurons in vitro. Neurosci Lett. 2009;461(1):1–6. doi:
- Ziello JE, Jovin IS, Huang Y. Hypoxia-Inducible Factor (HIF)-1 regulatory pathway and its potential for therapeutic intervention in malignancy and ischemia. Yale J Biol Med. 2007;80(2):51–60.
- Cross MJ, Dixelius J, Matsumoto T, et al. VEGF-receptor signal transduction. Trends Biochem Sci. 2003;28(9):488–494.
- Ferrara N, Gerber H-P, LeCouter J. The biology of VEGF and its receptors. Nat Med. 2003;9(6):669–676.
- Liu Y, Gray NS. Rational design of inhibitors that bind to inactive kinase conformations. Nat Chem Biol. 2006;2(7):358–364.
- Peng Y, Shiao H, Tu C, et al. Protein kinase inhibitor design by targeting the Asp-Phe-Gly (DFG) motif: the role of the DFG motif in the design of epidermal growth factor receptor inhibitors. J Med Chem. 2013;56:3889–3903.
- Wu P, Nielsen TE, Clausen MH. FDA-approved small-molecule kinase inhibitors. Trends Pharmacol Sci. 2015;36(7):422–439..
- Lu T, Zhang D, Chen Y, et al. Discovery of novel potent VEGFR-2 inhibitors exerting significant antiproliferative activity against cancer cell lines. J Med Chem. 2017;61:140–157.
- Eskens F, Verweij J. The clinical toxicity profile of vascular endothelial growth factor (VEGF) and vascular endothelial growth factor receptor (VEGFR) targeting angiogenesis inhibitors; a review. Eur J Cancer. 2006;42(18):3127–3139.
- Liu H, Chen Y, Lu S, et al. An integrated virtual screening approach for VEGFR-2 inhibitors. J Chem Inf Model. 2013;53:3163–3177.
- Boyer S. Small molecule inhibitors of KDR (VEGFR-2) kinase: an overview of structure activity relationships. CTMC. 2002;2(9):973–1000.
- Ebadi A, Razzaghi-Asl N, Shahabipour S, et al. Ab-initio and conformational analysis of a potent VEGFR-2 inhibitor: a case study on Motesanib. Iran J Pharm Res. 2014;13(2):405–415.
- Mctigue M, William B, Chen JH, et al. Molecular conformations, interactions, and properties associated with drug efficiency and clinical performance among VEGFR TK inhibitors. Proc Natl Acad Sci USA. 2012;109(45):18281–18289.
- Adzhigirey M, Day T, Sherman W, et al. Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J Comput Aided Mol Des. 2013;27:221–234.
- Shan Y, Shao X, Wang J, et al. Discovery of novel VEGFR-2 inhibitors. Part 5: exploration of diverse hinge-binding fragments via core-refining approach. Eur J Med Chem. 2015;103:80–90.
- Dixon SL, Smondyrev AM, Rao SN. PHASE: a novel approach to pharmacophore modeling and 3D database searching. Chem Biol Drug Des. 2006;67(5):370–372.
- Gupta N, Sitwala N, Patel K. Pharmacophore modelling, validation, 3D virtual screening, docking, design and in silico ADMET simulation study of histone deacetylase class-1 inhibitors. Med Chem Res. 2014;23(11):4853–4864.
- Coleman RG, Irwin JJ, Bolstad ES, et al. ZINC: a free tool to discover chemistry for biology. J Chem Inf Model. 2012;52:1757–1768.
- Sterling T, Irwin JJ. ZINC 15 - ligand discovery for everyone. J Chem Inf Model. 2015;55(11):2324–2337.
- Ihlenfeldt W. D, Voigt JH, Bienfait B, et al. Enhanced CACTVS browser of the open NCI database. J Chem Inf Comput Sci. 2002;42(1):46–57.
- Nicklaus MC, Milne GWA, Wang S, et al. National Cancer Institute drug information system 3D database. J Chem Inf Model. 2005;34:1219–1224.
- Repasky MP, Mainz DT, Greenwood JR, et al. Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein − ligand complexes. J Med Chem. 2006;49:6177–6196.
- Banks JL, Beard HS, Friesner RA, et al. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem. 2004;47:1739–1749. doi:10.1021/jm0306430.
- Genheden S, Ryde U. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin Drug Discov. 2015;10(5):449–461.
- Ntie-Kang F. An in silico evaluation of the ADMET profile of the StreptomeDB database. Springerplus. 2013;2(1):1–11.
- Gelpi J, Hospital A, Goñi R, et al. Molecular dynamics simulations: advances and applications. Adv Appl Bioinform Chem. 2015;8:37.
- Klepeis JL, Shaw DE, Dror RO, et al. Molecular dynamics—scalable algorithms for molecular dynamics simulations on commodity clusters. In: SC'06. Proceedings of the 2006 ACM/IEEE Conference on Supercomputing; 2006. DOI:10.1145/1188455.1188544
- Hou T, Wang J, Li Y, et al. Assessing the Performance of the MM/PBSA and MM/GBSA Methods. 1. The Accuracy of Binding Free Energy Calculations Based on Molecular Dynamics Simulations. J Chem Inf Model. . 2011;51(1):69–82.
- Hou T, Wang J, Li Y, et al. Assessing the performance of the MM/PBSA and MM/GBSA methods: II. The accuracy of ranking poses generated from docking. J Comput Chem. 2011;32(5):866–877.
- Lyne PD, Lamb ML, Saeh JC. Accurate prediction of the relative potencies of members of a series of kinase inhibitors using molecular docking and MM-GBSA scoring. J Med Chem. 2006;49(16):4805–4808.