Predicting the bioconcentration factor through a conformation-independent QSPR study
The ANTARES dataset is a large collection of known and verified experimental bioconcentration factor data, involving 851 highly heterogeneous compounds from which 159 are pesticides. The BCF ANTARES data were used to derive a conformation-independent QSPR model. A large set of 27,017 molecular descr...
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2017
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| Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_1062936X_v28_n9_p749_Aranda http://hdl.handle.net/20.500.12110/paper_1062936X_v28_n9_p749_Aranda |
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paper:paper_1062936X_v28_n9_p749_Aranda2023-06-08T16:03:30Z Predicting the bioconcentration factor through a conformation-independent QSPR study Bioconcentration factor (BCF) molecular descriptors pesticides quantitative structure-property relationships replacement method organic compound bioremediation chemical model chemistry conformation quantitative structure activity relation risk assessment statistical model Biodegradation, Environmental Linear Models Models, Chemical Molecular Conformation Organic Chemicals Quantitative Structure-Activity Relationship Risk Assessment The ANTARES dataset is a large collection of known and verified experimental bioconcentration factor data, involving 851 highly heterogeneous compounds from which 159 are pesticides. The BCF ANTARES data were used to derive a conformation-independent QSPR model. A large set of 27,017 molecular descriptors was explored, with the main intention of capturing the most relevant structural characteristics affecting the studied property. The structural descriptors were derived with different freeware tools, such as PaDEL, Epi Suite, CORAL, Mold2, RECON, and QuBiLs-MAS, and so it was interesting to find out the way that the different descriptor tools complemented each other in order to improve the statistical quality of the established QSPR. The best multivariable linear regression models were found with the Replacement Method variable sub-set selection technique. The proposed QSPR model improves previous reported models of the bioconcentration factor in the present dataset. © 2017 Informa UK Limited, trading as Taylor & Francis Group. 2017 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_1062936X_v28_n9_p749_Aranda http://hdl.handle.net/20.500.12110/paper_1062936X_v28_n9_p749_Aranda |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
Bioconcentration factor (BCF) molecular descriptors pesticides quantitative structure-property relationships replacement method organic compound bioremediation chemical model chemistry conformation quantitative structure activity relation risk assessment statistical model Biodegradation, Environmental Linear Models Models, Chemical Molecular Conformation Organic Chemicals Quantitative Structure-Activity Relationship Risk Assessment |
| spellingShingle |
Bioconcentration factor (BCF) molecular descriptors pesticides quantitative structure-property relationships replacement method organic compound bioremediation chemical model chemistry conformation quantitative structure activity relation risk assessment statistical model Biodegradation, Environmental Linear Models Models, Chemical Molecular Conformation Organic Chemicals Quantitative Structure-Activity Relationship Risk Assessment Predicting the bioconcentration factor through a conformation-independent QSPR study |
| topic_facet |
Bioconcentration factor (BCF) molecular descriptors pesticides quantitative structure-property relationships replacement method organic compound bioremediation chemical model chemistry conformation quantitative structure activity relation risk assessment statistical model Biodegradation, Environmental Linear Models Models, Chemical Molecular Conformation Organic Chemicals Quantitative Structure-Activity Relationship Risk Assessment |
| description |
The ANTARES dataset is a large collection of known and verified experimental bioconcentration factor data, involving 851 highly heterogeneous compounds from which 159 are pesticides. The BCF ANTARES data were used to derive a conformation-independent QSPR model. A large set of 27,017 molecular descriptors was explored, with the main intention of capturing the most relevant structural characteristics affecting the studied property. The structural descriptors were derived with different freeware tools, such as PaDEL, Epi Suite, CORAL, Mold2, RECON, and QuBiLs-MAS, and so it was interesting to find out the way that the different descriptor tools complemented each other in order to improve the statistical quality of the established QSPR. The best multivariable linear regression models were found with the Replacement Method variable sub-set selection technique. The proposed QSPR model improves previous reported models of the bioconcentration factor in the present dataset. © 2017 Informa UK Limited, trading as Taylor & Francis Group. |
| title |
Predicting the bioconcentration factor through a conformation-independent QSPR study |
| title_short |
Predicting the bioconcentration factor through a conformation-independent QSPR study |
| title_full |
Predicting the bioconcentration factor through a conformation-independent QSPR study |
| title_fullStr |
Predicting the bioconcentration factor through a conformation-independent QSPR study |
| title_full_unstemmed |
Predicting the bioconcentration factor through a conformation-independent QSPR study |
| title_sort |
predicting the bioconcentration factor through a conformation-independent qspr study |
| publishDate |
2017 |
| url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_1062936X_v28_n9_p749_Aranda http://hdl.handle.net/20.500.12110/paper_1062936X_v28_n9_p749_Aranda |
| _version_ |
1768543857356570624 |