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Dataset on aquatic ecotoxicity predictions of 2697 chemicals, using three quantitative structure-activity relationship platforms
Empirical and in silico data on the aquatic ecotoxicology of 2697 organic chemicals were collected in order to compile a dataset for assessing the predictive power of current Quantitative Structure Activity Relationship (QSAR) models and software platforms. This document presents the dataset and the...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10641136/ https://www.ncbi.nlm.nih.gov/pubmed/37965605 http://dx.doi.org/10.1016/j.dib.2023.109719 |
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author | Svedberg, Patrik Inostroza, Pedro A. Gustavsson, Mikael Kristiansson, Erik Spilsbury, Francis Backhaus, Thomas |
author_facet | Svedberg, Patrik Inostroza, Pedro A. Gustavsson, Mikael Kristiansson, Erik Spilsbury, Francis Backhaus, Thomas |
author_sort | Svedberg, Patrik |
collection | PubMed |
description | Empirical and in silico data on the aquatic ecotoxicology of 2697 organic chemicals were collected in order to compile a dataset for assessing the predictive power of current Quantitative Structure Activity Relationship (QSAR) models and software platforms. This document presents the dataset and the data pipeline for its creation. Empirical data were collected from the US EPA ECOTOX Knowledgebase (ECOTOX) and the EFSA (European Food Safety Authority) report “Completion of data entry of pesticide ecotoxicology Tier 1 study endpoints in a XML schema – database”. Only data for OECD recommended algae, daphnia and fish species were retained. QSAR toxicity predictions were calculated for each chemical and each of six endpoints using ECOSAR, VEGA and the Toxicity Estimation Software Tool (T.E.S.T.) platforms. Finally, the dataset was amended with SMILES, InChIKey, pKa and logP collected from webchem and PubChem. |
format | Online Article Text |
id | pubmed-10641136 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-106411362023-11-14 Dataset on aquatic ecotoxicity predictions of 2697 chemicals, using three quantitative structure-activity relationship platforms Svedberg, Patrik Inostroza, Pedro A. Gustavsson, Mikael Kristiansson, Erik Spilsbury, Francis Backhaus, Thomas Data Brief Data Article Empirical and in silico data on the aquatic ecotoxicology of 2697 organic chemicals were collected in order to compile a dataset for assessing the predictive power of current Quantitative Structure Activity Relationship (QSAR) models and software platforms. This document presents the dataset and the data pipeline for its creation. Empirical data were collected from the US EPA ECOTOX Knowledgebase (ECOTOX) and the EFSA (European Food Safety Authority) report “Completion of data entry of pesticide ecotoxicology Tier 1 study endpoints in a XML schema – database”. Only data for OECD recommended algae, daphnia and fish species were retained. QSAR toxicity predictions were calculated for each chemical and each of six endpoints using ECOSAR, VEGA and the Toxicity Estimation Software Tool (T.E.S.T.) platforms. Finally, the dataset was amended with SMILES, InChIKey, pKa and logP collected from webchem and PubChem. Elsevier 2023-10-24 /pmc/articles/PMC10641136/ /pubmed/37965605 http://dx.doi.org/10.1016/j.dib.2023.109719 Text en © 2023 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Data Article Svedberg, Patrik Inostroza, Pedro A. Gustavsson, Mikael Kristiansson, Erik Spilsbury, Francis Backhaus, Thomas Dataset on aquatic ecotoxicity predictions of 2697 chemicals, using three quantitative structure-activity relationship platforms |
title | Dataset on aquatic ecotoxicity predictions of 2697 chemicals, using three quantitative structure-activity relationship platforms |
title_full | Dataset on aquatic ecotoxicity predictions of 2697 chemicals, using three quantitative structure-activity relationship platforms |
title_fullStr | Dataset on aquatic ecotoxicity predictions of 2697 chemicals, using three quantitative structure-activity relationship platforms |
title_full_unstemmed | Dataset on aquatic ecotoxicity predictions of 2697 chemicals, using three quantitative structure-activity relationship platforms |
title_short | Dataset on aquatic ecotoxicity predictions of 2697 chemicals, using three quantitative structure-activity relationship platforms |
title_sort | dataset on aquatic ecotoxicity predictions of 2697 chemicals, using three quantitative structure-activity relationship platforms |
topic | Data Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10641136/ https://www.ncbi.nlm.nih.gov/pubmed/37965605 http://dx.doi.org/10.1016/j.dib.2023.109719 |
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