Trust, but Verify II: A Practical Guide to Chemogenomics Data Curation
Open accessAbstract
There is a growing public concern about the lack of reproducibility of experimental data published in peer-reviewed scientific literature. Herein, we review the most recent alerts regarding experimental data quality and discuss initiatives taken thus far to address this problem, especially in the area of chemical genomics. Going beyond just acknowledging the issue, we propose a chemical and biological data curation workflow that relies on existing cheminformatics approaches to flag, and when appropriate, correct possibly erroneous entries in large chemogenomics data sets. We posit that the adherence to the best practices for data curation is important for both experimental scientists who generate primary data and deposit them in chemical genomics databases and computational researchers who rely on these data for model development.
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QSAR modeling was one of the first research fields that highlighted the importance of data curation, rigorous validation of developed models, and data reproducibility.
“Importantly, QSAR modeling was one of the first research fields that highlighted the importance of data curation,13 rigorous validation of developed models,14 and data reproducibility,15 that has recently become a significant concern to the scientific community.16”
From QSAR without borders (2020) · cites this paper as [15]
Strict data curation prior to model development is highly recommended, whereas external validation ensures the stability and robustness of the models over all modeling and external prediction sets.
“Strict data curation prior to model development is highly recommended,15 whereas external validation ensures the stability and robustness of the models over all modeling and external prediction sets.”
From QSAR without borders (2020) · cites this paper as [15]
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“Begley and Ellis 13 discussed a similar analysis performed at Amgen, yielding an even lower rate of reproducibility of 11%.”
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- Authors
- Fourches, D., Muratov, E., & Tropsha, A.
- Affiliations
- North Carolina State University (US)
- University of North Carolina at Chapel Hill (US)
- Corresponding author
- Fourches, Denis
- Tropsha, Alexander
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2016-06-22
- Volume
- 56
- Issue
- 7
- Pages
- 1243-1252
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- PMC
- PMC5657146
- OpenAlex
- W2412746331
- Cited by
- 294
- References
- 4
- Citations by year
- 2026: 25 · 2025: 22 · 2024: 36 · 2023: 28 · 2022: 30 · 2021: 26 · 2020: 38 · 2019: 29 · 2018: 35 · 2017: 19 · 2016: 4
- FWCI
- 9.28
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Chemical Information and Modeling (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Carolina Digital Repository (University of North Carolina at Chapel Hill) (acceptedVersion)
- UNC Libraries
- Funders
- National Institute of General Medical Sciences (GM096967, GM66940)
- Office of the Executive Vice Chancellor and Provost, North Carolina State University
- Topics
- Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
- Keywords
- chemogenomics, data curation, reproducibility, data quality, cheminformatics, data curation workflow, data errors
- MeSH terms
- Quality Control, Statistics as Topic, Reproducibility of Results, Quantitative Structure-Activity Relationship, Genomics, Consensus
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