A molecular modeling study of combretastatin-like chalcones as anticancer agents using PLS, ANN and consensus models

Abstract Combretastatin-like chalcones are promising anticancer compounds that inhibit the mitotic process through interactions with β-tubulin. A detailed study of these compounds can contribute for the rational drug design of new structures aiming at compounds with high biological activity. For thi...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Lipinski, Célio Fernando [verfasserIn] Oliveira, Aline Alves [verfasserIn] Honorio, Kathia Maria [verfasserIn] Oliveira, Patrícia Rufino [verfasserIn] da Silva, Albérico Borges Ferreira [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Cancer Microtubules Chalcones QSAR ANN Consensus modeling

Übergeordnetes Werk:	Enthalten in: Structural chemistry - Dordrecht : Springer Science Business Media B.V., 1990, 29(2018), 4 vom: 22. Feb., Seite 957-965
Übergeordnetes Werk:	volume:29 ; year:2018 ; number:4 ; day:22 ; month:02 ; pages:957-965

Links:	Volltext

DOI / URN:	10.1007/s11224-017-1072-2

Katalog-ID:	SPR017891302

Internformat


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245	1	2	\|a A molecular modeling study of combretastatin-like chalcones as anticancer agents using PLS, ANN and consensus models
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520			\|a Abstract Combretastatin-like chalcones are promising anticancer compounds that inhibit the mitotic process through interactions with β-tubulin. A detailed study of these compounds can contribute for the rational drug design of new structures aiming at compounds with high biological activity. For this purpose, we have studied 87 combretastatin-like chalcones and proposed multivariate models based on partial least squares (PLS), artificial neural network consensus model (ANN-CM), and general consensus model (GCM). The proposed models have showed good predictive ability with r2test = 0.812 and MSE (test set) = 0.327 for the PLS model, r2test = 0.829 and MSE (test set) = 0.286 for the ANN-CM, and r2test = 0.822 and and MSE (test set) = 0.302 for the GCM. The selected molecular and electronic descriptors (RDF045e, RTv, RDF155u, RDF035m, SP02, PI, UNIP and $ E_{HOMO-3} $) represent molecular features of the compounds that can be associated to the biological activity and can be employed to help the design of new bioactive ligands with improved biological activity.
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700	1		\|a Oliveira, Aline Alves \|e verfasserin \|4 aut
700	1		\|a Honorio, Kathia Maria \|e verfasserin \|4 aut
700	1		\|a Oliveira, Patrícia Rufino \|e verfasserin \|4 aut
700	1		\|a da Silva, Albérico Borges Ferreira \|e verfasserin \|4 aut
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matchkey_str	article:15729001:2018----::mlclroeigtdocmrtsailkcacnssniacrgnss
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publishDate	2018
allfields	10.1007/s11224-017-1072-2 doi (DE-627)SPR017891302 (SPR)s11224-017-1072-2-e DE-627 ger DE-627 rakwb eng 540 ASE 35.00 bkl Lipinski, Célio Fernando verfasserin aut A molecular modeling study of combretastatin-like chalcones as anticancer agents using PLS, ANN and consensus models 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Combretastatin-like chalcones are promising anticancer compounds that inhibit the mitotic process through interactions with β-tubulin. A detailed study of these compounds can contribute for the rational drug design of new structures aiming at compounds with high biological activity. For this purpose, we have studied 87 combretastatin-like chalcones and proposed multivariate models based on partial least squares (PLS), artificial neural network consensus model (ANN-CM), and general consensus model (GCM). The proposed models have showed good predictive ability with r2test = 0.812 and MSE (test set) = 0.327 for the PLS model, r2test = 0.829 and MSE (test set) = 0.286 for the ANN-CM, and r2test = 0.822 and and MSE (test set) = 0.302 for the GCM. The selected molecular and electronic descriptors (RDF045e, RTv, RDF155u, RDF035m, SP02, PI, UNIP and $ E_{HOMO-3} $) represent molecular features of the compounds that can be associated to the biological activity and can be employed to help the design of new bioactive ligands with improved biological activity. Cancer (dpeaa)DE-He213 Microtubules (dpeaa)DE-He213 Chalcones (dpeaa)DE-He213 QSAR (dpeaa)DE-He213 ANN (dpeaa)DE-He213 Consensus modeling (dpeaa)DE-He213 Oliveira, Aline Alves verfasserin aut Honorio, Kathia Maria verfasserin aut Oliveira, Patrícia Rufino verfasserin aut da Silva, Albérico Borges Ferreira verfasserin aut Enthalten in Structural chemistry Dordrecht : Springer Science Business Media B.V., 1990 29(2018), 4 vom: 22. Feb., Seite 957-965 (DE-627)31886276X (DE-600)2018832-8 1572-9001 nnns volume:29 year:2018 number:4 day:22 month:02 pages:957-965 https://dx.doi.org/10.1007/s11224-017-1072-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 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_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 35.00 ASE AR 29 2018 4 22 02 957-965
spelling	10.1007/s11224-017-1072-2 doi (DE-627)SPR017891302 (SPR)s11224-017-1072-2-e DE-627 ger DE-627 rakwb eng 540 ASE 35.00 bkl Lipinski, Célio Fernando verfasserin aut A molecular modeling study of combretastatin-like chalcones as anticancer agents using PLS, ANN and consensus models 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Combretastatin-like chalcones are promising anticancer compounds that inhibit the mitotic process through interactions with β-tubulin. A detailed study of these compounds can contribute for the rational drug design of new structures aiming at compounds with high biological activity. For this purpose, we have studied 87 combretastatin-like chalcones and proposed multivariate models based on partial least squares (PLS), artificial neural network consensus model (ANN-CM), and general consensus model (GCM). The proposed models have showed good predictive ability with r2test = 0.812 and MSE (test set) = 0.327 for the PLS model, r2test = 0.829 and MSE (test set) = 0.286 for the ANN-CM, and r2test = 0.822 and and MSE (test set) = 0.302 for the GCM. The selected molecular and electronic descriptors (RDF045e, RTv, RDF155u, RDF035m, SP02, PI, UNIP and $ E_{HOMO-3} $) represent molecular features of the compounds that can be associated to the biological activity and can be employed to help the design of new bioactive ligands with improved biological activity. Cancer (dpeaa)DE-He213 Microtubules (dpeaa)DE-He213 Chalcones (dpeaa)DE-He213 QSAR (dpeaa)DE-He213 ANN (dpeaa)DE-He213 Consensus modeling (dpeaa)DE-He213 Oliveira, Aline Alves verfasserin aut Honorio, Kathia Maria verfasserin aut Oliveira, Patrícia Rufino verfasserin aut da Silva, Albérico Borges Ferreira verfasserin aut Enthalten in Structural chemistry Dordrecht : Springer Science Business Media B.V., 1990 29(2018), 4 vom: 22. Feb., Seite 957-965 (DE-627)31886276X (DE-600)2018832-8 1572-9001 nnns volume:29 year:2018 number:4 day:22 month:02 pages:957-965 https://dx.doi.org/10.1007/s11224-017-1072-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 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_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 35.00 ASE AR 29 2018 4 22 02 957-965
allfields_unstemmed	10.1007/s11224-017-1072-2 doi (DE-627)SPR017891302 (SPR)s11224-017-1072-2-e DE-627 ger DE-627 rakwb eng 540 ASE 35.00 bkl Lipinski, Célio Fernando verfasserin aut A molecular modeling study of combretastatin-like chalcones as anticancer agents using PLS, ANN and consensus models 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Combretastatin-like chalcones are promising anticancer compounds that inhibit the mitotic process through interactions with β-tubulin. A detailed study of these compounds can contribute for the rational drug design of new structures aiming at compounds with high biological activity. For this purpose, we have studied 87 combretastatin-like chalcones and proposed multivariate models based on partial least squares (PLS), artificial neural network consensus model (ANN-CM), and general consensus model (GCM). The proposed models have showed good predictive ability with r2test = 0.812 and MSE (test set) = 0.327 for the PLS model, r2test = 0.829 and MSE (test set) = 0.286 for the ANN-CM, and r2test = 0.822 and and MSE (test set) = 0.302 for the GCM. The selected molecular and electronic descriptors (RDF045e, RTv, RDF155u, RDF035m, SP02, PI, UNIP and $ E_{HOMO-3} $) represent molecular features of the compounds that can be associated to the biological activity and can be employed to help the design of new bioactive ligands with improved biological activity. Cancer (dpeaa)DE-He213 Microtubules (dpeaa)DE-He213 Chalcones (dpeaa)DE-He213 QSAR (dpeaa)DE-He213 ANN (dpeaa)DE-He213 Consensus modeling (dpeaa)DE-He213 Oliveira, Aline Alves verfasserin aut Honorio, Kathia Maria verfasserin aut Oliveira, Patrícia Rufino verfasserin aut da Silva, Albérico Borges Ferreira verfasserin aut Enthalten in Structural chemistry Dordrecht : Springer Science Business Media B.V., 1990 29(2018), 4 vom: 22. Feb., Seite 957-965 (DE-627)31886276X (DE-600)2018832-8 1572-9001 nnns volume:29 year:2018 number:4 day:22 month:02 pages:957-965 https://dx.doi.org/10.1007/s11224-017-1072-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 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_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 35.00 ASE AR 29 2018 4 22 02 957-965
allfieldsGer	10.1007/s11224-017-1072-2 doi (DE-627)SPR017891302 (SPR)s11224-017-1072-2-e DE-627 ger DE-627 rakwb eng 540 ASE 35.00 bkl Lipinski, Célio Fernando verfasserin aut A molecular modeling study of combretastatin-like chalcones as anticancer agents using PLS, ANN and consensus models 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Combretastatin-like chalcones are promising anticancer compounds that inhibit the mitotic process through interactions with β-tubulin. A detailed study of these compounds can contribute for the rational drug design of new structures aiming at compounds with high biological activity. For this purpose, we have studied 87 combretastatin-like chalcones and proposed multivariate models based on partial least squares (PLS), artificial neural network consensus model (ANN-CM), and general consensus model (GCM). The proposed models have showed good predictive ability with r2test = 0.812 and MSE (test set) = 0.327 for the PLS model, r2test = 0.829 and MSE (test set) = 0.286 for the ANN-CM, and r2test = 0.822 and and MSE (test set) = 0.302 for the GCM. The selected molecular and electronic descriptors (RDF045e, RTv, RDF155u, RDF035m, SP02, PI, UNIP and $ E_{HOMO-3} $) represent molecular features of the compounds that can be associated to the biological activity and can be employed to help the design of new bioactive ligands with improved biological activity. Cancer (dpeaa)DE-He213 Microtubules (dpeaa)DE-He213 Chalcones (dpeaa)DE-He213 QSAR (dpeaa)DE-He213 ANN (dpeaa)DE-He213 Consensus modeling (dpeaa)DE-He213 Oliveira, Aline Alves verfasserin aut Honorio, Kathia Maria verfasserin aut Oliveira, Patrícia Rufino verfasserin aut da Silva, Albérico Borges Ferreira verfasserin aut Enthalten in Structural chemistry Dordrecht : Springer Science Business Media B.V., 1990 29(2018), 4 vom: 22. Feb., Seite 957-965 (DE-627)31886276X (DE-600)2018832-8 1572-9001 nnns volume:29 year:2018 number:4 day:22 month:02 pages:957-965 https://dx.doi.org/10.1007/s11224-017-1072-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 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_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 35.00 ASE AR 29 2018 4 22 02 957-965
allfieldsSound	10.1007/s11224-017-1072-2 doi (DE-627)SPR017891302 (SPR)s11224-017-1072-2-e DE-627 ger DE-627 rakwb eng 540 ASE 35.00 bkl Lipinski, Célio Fernando verfasserin aut A molecular modeling study of combretastatin-like chalcones as anticancer agents using PLS, ANN and consensus models 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Combretastatin-like chalcones are promising anticancer compounds that inhibit the mitotic process through interactions with β-tubulin. A detailed study of these compounds can contribute for the rational drug design of new structures aiming at compounds with high biological activity. For this purpose, we have studied 87 combretastatin-like chalcones and proposed multivariate models based on partial least squares (PLS), artificial neural network consensus model (ANN-CM), and general consensus model (GCM). The proposed models have showed good predictive ability with r2test = 0.812 and MSE (test set) = 0.327 for the PLS model, r2test = 0.829 and MSE (test set) = 0.286 for the ANN-CM, and r2test = 0.822 and and MSE (test set) = 0.302 for the GCM. The selected molecular and electronic descriptors (RDF045e, RTv, RDF155u, RDF035m, SP02, PI, UNIP and $ E_{HOMO-3} $) represent molecular features of the compounds that can be associated to the biological activity and can be employed to help the design of new bioactive ligands with improved biological activity. Cancer (dpeaa)DE-He213 Microtubules (dpeaa)DE-He213 Chalcones (dpeaa)DE-He213 QSAR (dpeaa)DE-He213 ANN (dpeaa)DE-He213 Consensus modeling (dpeaa)DE-He213 Oliveira, Aline Alves verfasserin aut Honorio, Kathia Maria verfasserin aut Oliveira, Patrícia Rufino verfasserin aut da Silva, Albérico Borges Ferreira verfasserin aut Enthalten in Structural chemistry Dordrecht : Springer Science Business Media B.V., 1990 29(2018), 4 vom: 22. Feb., Seite 957-965 (DE-627)31886276X (DE-600)2018832-8 1572-9001 nnns volume:29 year:2018 number:4 day:22 month:02 pages:957-965 https://dx.doi.org/10.1007/s11224-017-1072-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 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_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 35.00 ASE AR 29 2018 4 22 02 957-965
language	English
source	Enthalten in Structural chemistry 29(2018), 4 vom: 22. Feb., Seite 957-965 volume:29 year:2018 number:4 day:22 month:02 pages:957-965
sourceStr	Enthalten in Structural chemistry 29(2018), 4 vom: 22. Feb., Seite 957-965 volume:29 year:2018 number:4 day:22 month:02 pages:957-965
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Cancer Microtubules Chalcones QSAR ANN Consensus modeling
dewey-raw	540
isfreeaccess_bool	false
container_title	Structural chemistry
authorswithroles_txt_mv	Lipinski, Célio Fernando @@aut@@ Oliveira, Aline Alves @@aut@@ Honorio, Kathia Maria @@aut@@ Oliveira, Patrícia Rufino @@aut@@ da Silva, Albérico Borges Ferreira @@aut@@
publishDateDaySort_date	2018-02-22T00:00:00Z
hierarchy_top_id	31886276X
dewey-sort	3540
id	SPR017891302
language_de	englisch
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author	Lipinski, Célio Fernando
spellingShingle	Lipinski, Célio Fernando ddc 540 bkl 35.00 misc Cancer misc Microtubules misc Chalcones misc QSAR misc ANN misc Consensus modeling A molecular modeling study of combretastatin-like chalcones as anticancer agents using PLS, ANN and consensus models
authorStr	Lipinski, Célio Fernando
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topic_title	540 ASE 35.00 bkl A molecular modeling study of combretastatin-like chalcones as anticancer agents using PLS, ANN and consensus models Cancer (dpeaa)DE-He213 Microtubules (dpeaa)DE-He213 Chalcones (dpeaa)DE-He213 QSAR (dpeaa)DE-He213 ANN (dpeaa)DE-He213 Consensus modeling (dpeaa)DE-He213
topic	ddc 540 bkl 35.00 misc Cancer misc Microtubules misc Chalcones misc QSAR misc ANN misc Consensus modeling
topic_unstemmed	ddc 540 bkl 35.00 misc Cancer misc Microtubules misc Chalcones misc QSAR misc ANN misc Consensus modeling
topic_browse	ddc 540 bkl 35.00 misc Cancer misc Microtubules misc Chalcones misc QSAR misc ANN misc Consensus modeling
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title	A molecular modeling study of combretastatin-like chalcones as anticancer agents using PLS, ANN and consensus models
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title_full	A molecular modeling study of combretastatin-like chalcones as anticancer agents using PLS, ANN and consensus models
author_sort	Lipinski, Célio Fernando
journal	Structural chemistry
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author_browse	Lipinski, Célio Fernando Oliveira, Aline Alves Honorio, Kathia Maria Oliveira, Patrícia Rufino da Silva, Albérico Borges Ferreira
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author-letter	Lipinski, Célio Fernando
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title_sort	molecular modeling study of combretastatin-like chalcones as anticancer agents using pls, ann and consensus models
title_auth	A molecular modeling study of combretastatin-like chalcones as anticancer agents using PLS, ANN and consensus models
abstract	Abstract Combretastatin-like chalcones are promising anticancer compounds that inhibit the mitotic process through interactions with β-tubulin. A detailed study of these compounds can contribute for the rational drug design of new structures aiming at compounds with high biological activity. For this purpose, we have studied 87 combretastatin-like chalcones and proposed multivariate models based on partial least squares (PLS), artificial neural network consensus model (ANN-CM), and general consensus model (GCM). The proposed models have showed good predictive ability with r2test = 0.812 and MSE (test set) = 0.327 for the PLS model, r2test = 0.829 and MSE (test set) = 0.286 for the ANN-CM, and r2test = 0.822 and and MSE (test set) = 0.302 for the GCM. The selected molecular and electronic descriptors (RDF045e, RTv, RDF155u, RDF035m, SP02, PI, UNIP and $ E_{HOMO-3} $) represent molecular features of the compounds that can be associated to the biological activity and can be employed to help the design of new bioactive ligands with improved biological activity.
abstractGer	Abstract Combretastatin-like chalcones are promising anticancer compounds that inhibit the mitotic process through interactions with β-tubulin. A detailed study of these compounds can contribute for the rational drug design of new structures aiming at compounds with high biological activity. For this purpose, we have studied 87 combretastatin-like chalcones and proposed multivariate models based on partial least squares (PLS), artificial neural network consensus model (ANN-CM), and general consensus model (GCM). The proposed models have showed good predictive ability with r2test = 0.812 and MSE (test set) = 0.327 for the PLS model, r2test = 0.829 and MSE (test set) = 0.286 for the ANN-CM, and r2test = 0.822 and and MSE (test set) = 0.302 for the GCM. The selected molecular and electronic descriptors (RDF045e, RTv, RDF155u, RDF035m, SP02, PI, UNIP and $ E_{HOMO-3} $) represent molecular features of the compounds that can be associated to the biological activity and can be employed to help the design of new bioactive ligands with improved biological activity.
abstract_unstemmed	Abstract Combretastatin-like chalcones are promising anticancer compounds that inhibit the mitotic process through interactions with β-tubulin. A detailed study of these compounds can contribute for the rational drug design of new structures aiming at compounds with high biological activity. For this purpose, we have studied 87 combretastatin-like chalcones and proposed multivariate models based on partial least squares (PLS), artificial neural network consensus model (ANN-CM), and general consensus model (GCM). The proposed models have showed good predictive ability with r2test = 0.812 and MSE (test set) = 0.327 for the PLS model, r2test = 0.829 and MSE (test set) = 0.286 for the ANN-CM, and r2test = 0.822 and and MSE (test set) = 0.302 for the GCM. The selected molecular and electronic descriptors (RDF045e, RTv, RDF155u, RDF035m, SP02, PI, UNIP and $ E_{HOMO-3} $) represent molecular features of the compounds that can be associated to the biological activity and can be employed to help the design of new bioactive ligands with improved biological activity.
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title_short	A molecular modeling study of combretastatin-like chalcones as anticancer agents using PLS, ANN and consensus models
url	https://dx.doi.org/10.1007/s11224-017-1072-2
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author2	Oliveira, Aline Alves Honorio, Kathia Maria Oliveira, Patrícia Rufino da Silva, Albérico Borges Ferreira
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doi_str	10.1007/s11224-017-1072-2
up_date	2024-07-03T15:54:03.249Z
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A molecular modeling study of combretastatin-like chalcones as anticancer agents using PLS, ANN and consensus models

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