Molecular

Pharmacophore modeling studies were undertaken for a series of quinoline derivatives as VEGFR-2 tyrosine kinase inhibitors. A five-point pharmacophore with two hydrogen bond acceptors (A), one hydrogen bond donor (D), and two aromatic rings (R) as pharmacophore features was developed. The pharmacoph...
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Autor*in:	Vinod G. Ugale [verfasserIn] Harun M. Patel [verfasserIn] Sanjay J. Surana [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	VEGFR-2 tyrosine kinase inhibitors 3D QSAR, docking Lipinski’s rule of five

Übergeordnetes Werk:	In: Arabian Journal of Chemistry - Elsevier, 2016, 10(2017), S2, Seite S1980-S2003
Übergeordnetes Werk:	volume:10 ; year:2017 ; number:S2 ; pages:S1980-S2003

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.arabjc.2013.07.026

Katalog-ID:	DOAJ003150143

Internformat


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520			\|a Pharmacophore modeling studies were undertaken for a series of quinoline derivatives as VEGFR-2 tyrosine kinase inhibitors. A five-point pharmacophore with two hydrogen bond acceptors (A), one hydrogen bond donor (D), and two aromatic rings (R) as pharmacophore features was developed. The pharmacophore hypothesis yielded a statistically significant 3D-QSAR model, with a correlation coefficient of r2 = 0.8621 for training set compounds. The model generated showed excellent predictive power, with a correlation coefficient of q2 = 0.6943 and for a test set of compounds. Furthermore, the structure–activity relationships of quinoline derivatives as VEGFR-2 tyrosine kinase inhibitors were elucidated and the activity differences between them discussed. Docking studies were also carried out wherein active and inactive compounds were docked into the active site of the VEGFR-2 crystal structure to analyze drug-receptor interactions. Further we analyzed all the compounds for Lipinski’s rule of five to evaluate drug likeness and established in silico ADME parameters using QikProp. The results provide insights that will aid the optimization of these classes of VEGFR-2 inhibitors for better activity, and may prove helpful for further lead optimization and virtual screening.
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912			\|a GBV_ILN_285
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912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2026
912			\|a GBV_ILN_2027
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912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
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912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4338
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publishDate	2017
allfields	10.1016/j.arabjc.2013.07.026 doi (DE-627)DOAJ003150143 (DE-599)DOAJa2492fe050c84c0bb64d4ac3e17af575 DE-627 ger DE-627 rakwb eng QD1-999 Vinod G. Ugale verfasserin aut Molecular 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Pharmacophore modeling studies were undertaken for a series of quinoline derivatives as VEGFR-2 tyrosine kinase inhibitors. A five-point pharmacophore with two hydrogen bond acceptors (A), one hydrogen bond donor (D), and two aromatic rings (R) as pharmacophore features was developed. The pharmacophore hypothesis yielded a statistically significant 3D-QSAR model, with a correlation coefficient of r2 = 0.8621 for training set compounds. The model generated showed excellent predictive power, with a correlation coefficient of q2 = 0.6943 and for a test set of compounds. Furthermore, the structure–activity relationships of quinoline derivatives as VEGFR-2 tyrosine kinase inhibitors were elucidated and the activity differences between them discussed. Docking studies were also carried out wherein active and inactive compounds were docked into the active site of the VEGFR-2 crystal structure to analyze drug-receptor interactions. Further we analyzed all the compounds for Lipinski’s rule of five to evaluate drug likeness and established in silico ADME parameters using QikProp. The results provide insights that will aid the optimization of these classes of VEGFR-2 inhibitors for better activity, and may prove helpful for further lead optimization and virtual screening. VEGFR-2 tyrosine kinase inhibitors 3D QSAR, docking Lipinski’s rule of five Chemistry Harun M. Patel verfasserin aut Sanjay J. Surana verfasserin aut In Arabian Journal of Chemistry Elsevier, 2016 10(2017), S2, Seite S1980-S2003 (DE-627)609401564 (DE-600)2515214-2 18785352 nnns volume:10 year:2017 number:S2 pages:S1980-S2003 https://doi.org/10.1016/j.arabjc.2013.07.026 kostenfrei https://doaj.org/article/a2492fe050c84c0bb64d4ac3e17af575 kostenfrei http://www.sciencedirect.com/science/article/pii/S1878535213002268 kostenfrei https://doaj.org/toc/1878-5352 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 10 2017 S2 S1980-S2003
spelling	10.1016/j.arabjc.2013.07.026 doi (DE-627)DOAJ003150143 (DE-599)DOAJa2492fe050c84c0bb64d4ac3e17af575 DE-627 ger DE-627 rakwb eng QD1-999 Vinod G. Ugale verfasserin aut Molecular 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Pharmacophore modeling studies were undertaken for a series of quinoline derivatives as VEGFR-2 tyrosine kinase inhibitors. A five-point pharmacophore with two hydrogen bond acceptors (A), one hydrogen bond donor (D), and two aromatic rings (R) as pharmacophore features was developed. The pharmacophore hypothesis yielded a statistically significant 3D-QSAR model, with a correlation coefficient of r2 = 0.8621 for training set compounds. The model generated showed excellent predictive power, with a correlation coefficient of q2 = 0.6943 and for a test set of compounds. Furthermore, the structure–activity relationships of quinoline derivatives as VEGFR-2 tyrosine kinase inhibitors were elucidated and the activity differences between them discussed. Docking studies were also carried out wherein active and inactive compounds were docked into the active site of the VEGFR-2 crystal structure to analyze drug-receptor interactions. Further we analyzed all the compounds for Lipinski’s rule of five to evaluate drug likeness and established in silico ADME parameters using QikProp. The results provide insights that will aid the optimization of these classes of VEGFR-2 inhibitors for better activity, and may prove helpful for further lead optimization and virtual screening. VEGFR-2 tyrosine kinase inhibitors 3D QSAR, docking Lipinski’s rule of five Chemistry Harun M. Patel verfasserin aut Sanjay J. Surana verfasserin aut In Arabian Journal of Chemistry Elsevier, 2016 10(2017), S2, Seite S1980-S2003 (DE-627)609401564 (DE-600)2515214-2 18785352 nnns volume:10 year:2017 number:S2 pages:S1980-S2003 https://doi.org/10.1016/j.arabjc.2013.07.026 kostenfrei https://doaj.org/article/a2492fe050c84c0bb64d4ac3e17af575 kostenfrei http://www.sciencedirect.com/science/article/pii/S1878535213002268 kostenfrei https://doaj.org/toc/1878-5352 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 10 2017 S2 S1980-S2003
allfields_unstemmed	10.1016/j.arabjc.2013.07.026 doi (DE-627)DOAJ003150143 (DE-599)DOAJa2492fe050c84c0bb64d4ac3e17af575 DE-627 ger DE-627 rakwb eng QD1-999 Vinod G. Ugale verfasserin aut Molecular 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Pharmacophore modeling studies were undertaken for a series of quinoline derivatives as VEGFR-2 tyrosine kinase inhibitors. A five-point pharmacophore with two hydrogen bond acceptors (A), one hydrogen bond donor (D), and two aromatic rings (R) as pharmacophore features was developed. The pharmacophore hypothesis yielded a statistically significant 3D-QSAR model, with a correlation coefficient of r2 = 0.8621 for training set compounds. The model generated showed excellent predictive power, with a correlation coefficient of q2 = 0.6943 and for a test set of compounds. Furthermore, the structure–activity relationships of quinoline derivatives as VEGFR-2 tyrosine kinase inhibitors were elucidated and the activity differences between them discussed. Docking studies were also carried out wherein active and inactive compounds were docked into the active site of the VEGFR-2 crystal structure to analyze drug-receptor interactions. Further we analyzed all the compounds for Lipinski’s rule of five to evaluate drug likeness and established in silico ADME parameters using QikProp. The results provide insights that will aid the optimization of these classes of VEGFR-2 inhibitors for better activity, and may prove helpful for further lead optimization and virtual screening. VEGFR-2 tyrosine kinase inhibitors 3D QSAR, docking Lipinski’s rule of five Chemistry Harun M. Patel verfasserin aut Sanjay J. Surana verfasserin aut In Arabian Journal of Chemistry Elsevier, 2016 10(2017), S2, Seite S1980-S2003 (DE-627)609401564 (DE-600)2515214-2 18785352 nnns volume:10 year:2017 number:S2 pages:S1980-S2003 https://doi.org/10.1016/j.arabjc.2013.07.026 kostenfrei https://doaj.org/article/a2492fe050c84c0bb64d4ac3e17af575 kostenfrei http://www.sciencedirect.com/science/article/pii/S1878535213002268 kostenfrei https://doaj.org/toc/1878-5352 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 10 2017 S2 S1980-S2003
allfieldsGer	10.1016/j.arabjc.2013.07.026 doi (DE-627)DOAJ003150143 (DE-599)DOAJa2492fe050c84c0bb64d4ac3e17af575 DE-627 ger DE-627 rakwb eng QD1-999 Vinod G. Ugale verfasserin aut Molecular 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Pharmacophore modeling studies were undertaken for a series of quinoline derivatives as VEGFR-2 tyrosine kinase inhibitors. A five-point pharmacophore with two hydrogen bond acceptors (A), one hydrogen bond donor (D), and two aromatic rings (R) as pharmacophore features was developed. The pharmacophore hypothesis yielded a statistically significant 3D-QSAR model, with a correlation coefficient of r2 = 0.8621 for training set compounds. The model generated showed excellent predictive power, with a correlation coefficient of q2 = 0.6943 and for a test set of compounds. Furthermore, the structure–activity relationships of quinoline derivatives as VEGFR-2 tyrosine kinase inhibitors were elucidated and the activity differences between them discussed. Docking studies were also carried out wherein active and inactive compounds were docked into the active site of the VEGFR-2 crystal structure to analyze drug-receptor interactions. Further we analyzed all the compounds for Lipinski’s rule of five to evaluate drug likeness and established in silico ADME parameters using QikProp. The results provide insights that will aid the optimization of these classes of VEGFR-2 inhibitors for better activity, and may prove helpful for further lead optimization and virtual screening. VEGFR-2 tyrosine kinase inhibitors 3D QSAR, docking Lipinski’s rule of five Chemistry Harun M. Patel verfasserin aut Sanjay J. Surana verfasserin aut In Arabian Journal of Chemistry Elsevier, 2016 10(2017), S2, Seite S1980-S2003 (DE-627)609401564 (DE-600)2515214-2 18785352 nnns volume:10 year:2017 number:S2 pages:S1980-S2003 https://doi.org/10.1016/j.arabjc.2013.07.026 kostenfrei https://doaj.org/article/a2492fe050c84c0bb64d4ac3e17af575 kostenfrei http://www.sciencedirect.com/science/article/pii/S1878535213002268 kostenfrei https://doaj.org/toc/1878-5352 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 10 2017 S2 S1980-S2003
allfieldsSound	10.1016/j.arabjc.2013.07.026 doi (DE-627)DOAJ003150143 (DE-599)DOAJa2492fe050c84c0bb64d4ac3e17af575 DE-627 ger DE-627 rakwb eng QD1-999 Vinod G. Ugale verfasserin aut Molecular 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Pharmacophore modeling studies were undertaken for a series of quinoline derivatives as VEGFR-2 tyrosine kinase inhibitors. A five-point pharmacophore with two hydrogen bond acceptors (A), one hydrogen bond donor (D), and two aromatic rings (R) as pharmacophore features was developed. The pharmacophore hypothesis yielded a statistically significant 3D-QSAR model, with a correlation coefficient of r2 = 0.8621 for training set compounds. The model generated showed excellent predictive power, with a correlation coefficient of q2 = 0.6943 and for a test set of compounds. Furthermore, the structure–activity relationships of quinoline derivatives as VEGFR-2 tyrosine kinase inhibitors were elucidated and the activity differences between them discussed. Docking studies were also carried out wherein active and inactive compounds were docked into the active site of the VEGFR-2 crystal structure to analyze drug-receptor interactions. Further we analyzed all the compounds for Lipinski’s rule of five to evaluate drug likeness and established in silico ADME parameters using QikProp. The results provide insights that will aid the optimization of these classes of VEGFR-2 inhibitors for better activity, and may prove helpful for further lead optimization and virtual screening. VEGFR-2 tyrosine kinase inhibitors 3D QSAR, docking Lipinski’s rule of five Chemistry Harun M. Patel verfasserin aut Sanjay J. Surana verfasserin aut In Arabian Journal of Chemistry Elsevier, 2016 10(2017), S2, Seite S1980-S2003 (DE-627)609401564 (DE-600)2515214-2 18785352 nnns volume:10 year:2017 number:S2 pages:S1980-S2003 https://doi.org/10.1016/j.arabjc.2013.07.026 kostenfrei https://doaj.org/article/a2492fe050c84c0bb64d4ac3e17af575 kostenfrei http://www.sciencedirect.com/science/article/pii/S1878535213002268 kostenfrei https://doaj.org/toc/1878-5352 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 10 2017 S2 S1980-S2003
language	English
source	In Arabian Journal of Chemistry 10(2017), S2, Seite S1980-S2003 volume:10 year:2017 number:S2 pages:S1980-S2003
sourceStr	In Arabian Journal of Chemistry 10(2017), S2, Seite S1980-S2003 volume:10 year:2017 number:S2 pages:S1980-S2003
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	VEGFR-2 tyrosine kinase inhibitors 3D QSAR, docking Lipinski’s rule of five Chemistry
isfreeaccess_bool	true
container_title	Arabian Journal of Chemistry
authorswithroles_txt_mv	Vinod G. Ugale @@aut@@ Harun M. Patel @@aut@@ Sanjay J. Surana @@aut@@
publishDateDaySort_date	2017-01-01T00:00:00Z
hierarchy_top_id	609401564
id	DOAJ003150143
language_de	englisch
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callnumber-first	Q - Science
author	Vinod G. Ugale
spellingShingle	Vinod G. Ugale misc QD1-999 misc VEGFR-2 tyrosine kinase inhibitors misc 3D QSAR, docking misc Lipinski’s rule of five misc Chemistry Molecular
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topic_title	QD1-999 Molecular VEGFR-2 tyrosine kinase inhibitors 3D QSAR, docking Lipinski’s rule of five
topic	misc QD1-999 misc VEGFR-2 tyrosine kinase inhibitors misc 3D QSAR, docking misc Lipinski’s rule of five misc Chemistry
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author_browse	Vinod G. Ugale Harun M. Patel Sanjay J. Surana
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abstract	Pharmacophore modeling studies were undertaken for a series of quinoline derivatives as VEGFR-2 tyrosine kinase inhibitors. A five-point pharmacophore with two hydrogen bond acceptors (A), one hydrogen bond donor (D), and two aromatic rings (R) as pharmacophore features was developed. The pharmacophore hypothesis yielded a statistically significant 3D-QSAR model, with a correlation coefficient of r2 = 0.8621 for training set compounds. The model generated showed excellent predictive power, with a correlation coefficient of q2 = 0.6943 and for a test set of compounds. Furthermore, the structure–activity relationships of quinoline derivatives as VEGFR-2 tyrosine kinase inhibitors were elucidated and the activity differences between them discussed. Docking studies were also carried out wherein active and inactive compounds were docked into the active site of the VEGFR-2 crystal structure to analyze drug-receptor interactions. Further we analyzed all the compounds for Lipinski’s rule of five to evaluate drug likeness and established in silico ADME parameters using QikProp. The results provide insights that will aid the optimization of these classes of VEGFR-2 inhibitors for better activity, and may prove helpful for further lead optimization and virtual screening.
abstractGer	Pharmacophore modeling studies were undertaken for a series of quinoline derivatives as VEGFR-2 tyrosine kinase inhibitors. A five-point pharmacophore with two hydrogen bond acceptors (A), one hydrogen bond donor (D), and two aromatic rings (R) as pharmacophore features was developed. The pharmacophore hypothesis yielded a statistically significant 3D-QSAR model, with a correlation coefficient of r2 = 0.8621 for training set compounds. The model generated showed excellent predictive power, with a correlation coefficient of q2 = 0.6943 and for a test set of compounds. Furthermore, the structure–activity relationships of quinoline derivatives as VEGFR-2 tyrosine kinase inhibitors were elucidated and the activity differences between them discussed. Docking studies were also carried out wherein active and inactive compounds were docked into the active site of the VEGFR-2 crystal structure to analyze drug-receptor interactions. Further we analyzed all the compounds for Lipinski’s rule of five to evaluate drug likeness and established in silico ADME parameters using QikProp. The results provide insights that will aid the optimization of these classes of VEGFR-2 inhibitors for better activity, and may prove helpful for further lead optimization and virtual screening.
abstract_unstemmed	Pharmacophore modeling studies were undertaken for a series of quinoline derivatives as VEGFR-2 tyrosine kinase inhibitors. A five-point pharmacophore with two hydrogen bond acceptors (A), one hydrogen bond donor (D), and two aromatic rings (R) as pharmacophore features was developed. The pharmacophore hypothesis yielded a statistically significant 3D-QSAR model, with a correlation coefficient of r2 = 0.8621 for training set compounds. The model generated showed excellent predictive power, with a correlation coefficient of q2 = 0.6943 and for a test set of compounds. Furthermore, the structure–activity relationships of quinoline derivatives as VEGFR-2 tyrosine kinase inhibitors were elucidated and the activity differences between them discussed. Docking studies were also carried out wherein active and inactive compounds were docked into the active site of the VEGFR-2 crystal structure to analyze drug-receptor interactions. Further we analyzed all the compounds for Lipinski’s rule of five to evaluate drug likeness and established in silico ADME parameters using QikProp. The results provide insights that will aid the optimization of these classes of VEGFR-2 inhibitors for better activity, and may prove helpful for further lead optimization and virtual screening.
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url	https://doi.org/10.1016/j.arabjc.2013.07.026 https://doaj.org/article/a2492fe050c84c0bb64d4ac3e17af575 http://www.sciencedirect.com/science/article/pii/S1878535213002268 https://doaj.org/toc/1878-5352
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score	7.400755

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