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

Overview of attention for article published in SAR and QSAR in Environmental Research, November 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#1 of 106)
  • High Attention Score compared to outputs of the same age (86th percentile)

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Title
An automated curation procedure for addressing chemical errors and inconsistencies in public datasets used in QSAR modelling $
Published in
SAR and QSAR in Environmental Research, November 2016
DOI 10.1080/1062936x.2016.1253611
Pubmed ID
Authors

K. Mansouri, C. M. Grulke, A. M. Richard, R. S. Judson, A. J. Williams

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.

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Attention Score in Context

This research output has an Altmetric Attention Score of 12. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 February 2017.
All research outputs
#614,650
of 7,269,337 outputs
Outputs from SAR and QSAR in Environmental Research
#1
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Outputs of similar age
#30,280
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Outputs of similar age from SAR and QSAR in Environmental Research
#1
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Altmetric has tracked 7,269,337 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 106 research outputs from this source. They receive a mean Attention Score of 1.7. This one has done particularly well, scoring higher than 99% of its peers.
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We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them