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Estimating Error Rates in Bioactivity Databases
- Source :
- Journal of Chemical Information and Modeling. 53:2499-2505
- Publication Year :
- 2013
- Publisher :
- American Chemical Society (ACS), 2013.
-
Abstract
- Bioactivity databases are routinely used in drug discovery to look-up and, using prediction tools, to predict potential targets for small molecules. These databases are typically manually curated from patents and scientific articles. Apart from errors in the source document, the human factor can cause errors during the extraction process. These errors can lead to wrong decisions in the early drug discovery process. In the current work, we have compared bioactivity data from three large databases (ChEMBL, Liceptor, and WOMBAT) who have curated data from the same source documents. As a result, we are able to report error rate estimates for individual activity parameters and individual bioactivity databases. Small molecule structures have the greatest estimated error rate followed by target, activity value, and activity type. This order is also reflected in supplier-specific error rate estimates. The results are also useful in identifying data points for recuration. We hope the results will lead to a more widespread awareness among scientists on the frequencies and types of errors in bioactivity data.
- Subjects :
- Database
Databases, Pharmaceutical
Drug discovery
Computer science
General Chemical Engineering
Proteins
Word error rate
General Chemistry
Library and Information Sciences
Ligands
chEMBL
computer.software_genre
Databases, Bibliographic
Computer Science Applications
Patents as Topic
Small Molecule Libraries
Data point
Bibliometrics
Drug Discovery
Humans
Data mining
Source document
Publication Bias
computer
Databases, Chemical
Subjects
Details
- ISSN :
- 1549960X and 15499596
- Volume :
- 53
- Database :
- OpenAIRE
- Journal :
- Journal of Chemical Information and Modeling
- Accession number :
- edsair.doi.dedup.....4fc4f5479a1eaf838cd6095f2fb5dabf