Estimating error rates in bioactivity databases.
J Chem Inf Model
; 53(10): 2499-505, 2013 Oct 28.
Article
en En
| MEDLINE
| ID: mdl-24160896
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.
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Proteínas
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Bibliometría
/
Sesgo de Publicación
/
Bibliotecas de Moléculas Pequeñas
/
Descubrimiento de Drogas
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Año:
2013
Tipo del documento:
Article