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SAR QSAR Environ Res. 2016 Nov;27(11):939-965.

An automated curation procedure for addressing chemical errors and inconsistencies in public datasets used in QSAR modelling.

Author information

1
a Oak Ridge Institute for Science and Education (ORISE) , Oak Ridge , TN , USA.
2
b US Environmental Protection Agency, Office of Research and Development , National Center for Computational Toxicology , Research Triangle Park, NC , USA.

Abstract

The increasing availability of large collections of chemical structures and associated experimental data provides an opportunity to build robust QSAR models for applications in different fields. One common concern is the quality of both the chemical structure information and associated experimental data. Here we describe the development of an automated KNIME workflow to curate and correct errors in the structure and identity of chemicals using the publicly available PHYSPROP physicochemical 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 with the larger curated and corrected dataset. The latter showed statistically improved predictive performance. The final workflow was used to curate the full list of PHYSPROP datasets, and is being made publicly available for further usage and integration by the scientific community.

KEYWORDS:

Open Data; QSAR modelling; data curation; physicochemical properties; standardization

PMID:
27885862
DOI:
10.1080/1062936X.2016.1253611
[Indexed for MEDLINE]

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