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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340923007904 |
| _version_ | 1797429979868299264 |
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| author | Patrik Svedberg Pedro A. Inostroza Mikael Gustavsson Erik Kristiansson Francis Spilsbury Thomas Backhaus |
| author_facet | Patrik Svedberg Pedro A. Inostroza Mikael Gustavsson Erik Kristiansson Francis Spilsbury Thomas Backhaus |
| author_sort | Patrik Svedberg |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T09:22:05Z |
| format | Article |
| id | doaj.art-581b1fe444b24efd81696a648ad6e012 |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-03-09T09:22:05Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-581b1fe444b24efd81696a648ad6e0122023-12-02T07:00:11ZengElsevierData in Brief2352-34092023-12-0151109719Dataset on aquatic ecotoxicity predictions of 2697 chemicals, using three quantitative structure-activity relationship platformsPatrik Svedberg0Pedro A. Inostroza1Mikael Gustavsson2Erik Kristiansson3Francis Spilsbury4Thomas Backhaus5Department of Biological and Environmental Sciences, University of Gothenburg, PO Box 463, SE-405 30 Gothenburg, Sweden; Corresponding author.Department of Biological and Environmental Sciences, University of Gothenburg, PO Box 463, SE-405 30 Gothenburg, Sweden; Institute for Environmental Research, RWTH Aachen University, D-52072 Aachen, GermanyDepartment of Biological and Environmental Sciences, University of Gothenburg, PO Box 463, SE-405 30 Gothenburg, Sweden; Department of Economics, University of Gothenburg, PO Box 640, SE-405 30 Gothenburg, SwedenDepartment of Mathematical Sciences, Chalmers University of Technology and University of Gothenburg, SE-412 96 Gothenburg, SwedenDepartment of Biological and Environmental Sciences, University of Gothenburg, PO Box 463, SE-405 30 Gothenburg, SwedenDepartment of Biological and Environmental Sciences, University of Gothenburg, PO Box 463, SE-405 30 Gothenburg, Sweden; Institute for Environmental Research, RWTH Aachen University, D-52072 Aachen, GermanyEmpirical 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.http://www.sciencedirect.com/science/article/pii/S2352340923007904Chemical toxicityQuantitative structure-activity relationshipECOSARToxicity estimation software toolVEGA |
| spellingShingle | Patrik Svedberg Pedro A. Inostroza Mikael Gustavsson Erik Kristiansson Francis Spilsbury Thomas Backhaus Dataset on aquatic ecotoxicity predictions of 2697 chemicals, using three quantitative structure-activity relationship platforms Data in Brief Chemical toxicity Quantitative structure-activity relationship ECOSAR Toxicity estimation software tool VEGA |
| 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 | Chemical toxicity Quantitative structure-activity relationship ECOSAR Toxicity estimation software tool VEGA |
| url | http://www.sciencedirect.com/science/article/pii/S2352340923007904 |
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