Dataset Identification:
Resource Abstract:
- description: Here we describe the development of an automated KNIME workflow to curate and correct errors in the structure
and identity of chemicals using the publically available PHYSPROP physico-chemical properties and environmental fate datasets.
The workflow first assembles structure-identity pairs using up to four provided chemical identifiers, including chemical name,
CASRNs, SMILES, and MolBlock. Problems detected included errors and mismatches in chemical structure formats, identifiers,
and various structure validation issues, including hypervalency and stereochemistry descriptions. Subsequently, a machine
learning procedure was applied to evaluate the impact of this curation process. The performance of QSAR models built on only
the highest quality subset of the original dataset was compared to the larger curated and corrected data set. The latter showed
statistically improved predictive performance. The final workflow was used to curate the full list of PHYSPROP datasets, and
is being made publically available for further usage and integration by the scientific community. This dataset is associated
with the following publication: Mansouri, K., C. Grulke, A. Richard, R. Judson, and A. Williams. (SAR AND QSAR IN ENVIRONMENTAL
RESEARCH) An automated curation procedure for addressing chemical errors and inconsistencies in public datasets used in QSAR
modeling. SAR AND QSAR IN ENVIRONMENTAL RESEARCH. Taylor & Francis, Inc., Philadelphia, PA, USA, 27(11): 911-937,
(2016).; abstract: Here we describe the development of an automated KNIME workflow to curate and correct errors in the structure
and identity of chemicals using the publically available PHYSPROP physico-chemical properties and environmental fate datasets.
The workflow first assembles structure-identity pairs using up to four provided chemical identifiers, including chemical name,
CASRNs, SMILES, and MolBlock. Problems detected included errors and mismatches in chemical structure formats, identifiers,
and various structure validation issues, including hypervalency and stereochemistry descriptions. Subsequently, a machine
learning procedure was applied to evaluate the impact of this curation process. The performance of QSAR models built on only
the highest quality subset of the original dataset was compared to the larger curated and corrected data set. The latter showed
statistically improved predictive performance. The final workflow was used to curate the full list of PHYSPROP datasets, and
is being made publically available for further usage and integration by the scientific community. This dataset is associated
with the following publication: Mansouri, K., C. Grulke, A. Richard, R. Judson, and A. Williams. (SAR AND QSAR IN ENVIRONMENTAL
RESEARCH) An automated curation procedure for addressing chemical errors and inconsistencies in public datasets used in QSAR
modeling. SAR AND QSAR IN ENVIRONMENTAL RESEARCH. Taylor & Francis, Inc., Philadelphia, PA, USA, 27(11): 911-937,
(2016).
Citation
- Title Judson_Mansouri_Automated_Chemical_Curation_QSAREnvRes_Data.
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- creation Date
2017-10-03T17:18:51.216590
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Metadata data stamp:
2018-08-07T00:02:58Z
Resource Maintenance Information
- maintenance or update frequency:
- notes: This metadata record was generated by an xslt transformation from a dc metadata record; Transform by Stephen M. Richard, based
on a transform by Damian Ulbricht. Run on 2018-08-07T00:02:58Z
Metadata contact
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pointOfContact
- organisation Name
CINERGI Metadata catalog
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- Contact information
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- Address
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- electronic Mail Address cinergi@sdsc.edu
Metadata language
eng
Metadata character set encoding:
utf8
Metadata standard for this record:
ISO 19139 Geographic Information - Metadata - Implementation Specification
standard version:
2007
Metadata record identifier:
urn:dciso:metadataabout:7a3dab95-33f2-4208-8968-cdbdfe43db5b
Metadata record format is ISO19139 XML (MD_Metadata)