ChemDataExtractor statistics

To access cutting-edge analytics on consensus tools, life science contexts and associated fields, you will need to subscribe to our premium service.


Citations per year

Citations chart

Popular tool citations

chevron_left Bio-entity identification chevron_right
Popular tools chart

Tool usage distribution map

Tool usage distribution map

Associated diseases

Associated diseases

ChemDataExtractor specifications


Unique identifier OMICS_15623
Name ChemDataExtractor
Software type Toolkit/Suite
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages Python
License MIT License
Computer skills Advanced
Version 1.3.0
Stability Stable
Maintained Yes



Add your version



Publication for ChemDataExtractor

ChemDataExtractor in publication

PMCID: 5595045
PMID: 28895943
DOI: 10.1038/sdata.2017.127

[…] the categorical accuracy of this neural network classifier, as measured against the same test set, is 86% and its f1 score is 81%. this is comparable to the performance achieved by the recent chemdataextractor model on a similar task, which is trained to extract relevant text from chemistry articles., shows the process of interpreting higher-level relations in the text, including […]

To access a full list of publications, you will need to upgrade to our premium service.

ChemDataExtractor institution(s)
Cavendish Laboratory, University of Cambridge, Cambridge, UK
ChemDataExtractor funding source(s)
This work was supported by the EPSRC for a DTA PhD studentship (Grant No. EP/J500380/1) and the 1851 Royal Commission for the 2014 Design Fellowship.

ChemDataExtractor reviews

star_border star_border star_border star_border star_border
star star star star star

Be the first to review ChemDataExtractor