Literature DB >> 21871970

A quality alert and call for improved curation of public chemistry databases.

Antony J Williams, Sean Ekins.   

Abstract

In the last ten years, public online databases have rapidly become trusted valuable resources upon which researchers rely for their chemical structures and data for use in cheminformatics, bioinformatics, systems biology, translational medicine and now drug repositioning or repurposing efforts. Their utility depends on the quality of the underlying molecular structures used. Unfortunately, the quality of much of the chemical structure-based data introduced to the public domain is poor. As an example we describe some of the errors found in the recently released NIH Chemical Genomics Center 'NPC browser' database as an example. There is an urgent need for government funded data curation to improve the quality of internet chemistry and to limit the proliferation of errors and wasted efforts.
Copyright © 2011 Elsevier Ltd. All rights reserved.

Mesh:

Year:  2011        PMID: 21871970     DOI: 10.1016/j.drudis.2011.07.007

Source DB:  PubMed          Journal:  Drug Discov Today        ISSN: 1359-6446            Impact factor:   7.851


  34 in total

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4.  The ChEMBL database as linked open data.

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Review 5.  Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling.

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6.  Public domain databases for medicinal chemistry.

Authors:  George Nicola; Tiqing Liu; Michael K Gilson
Journal:  J Med Chem       Date:  2012-07-11       Impact factor: 7.446

7.  Data sharing matters.

Authors:  Wendy A Warr
Journal:  J Comput Aided Mol Des       Date:  2014-01-17       Impact factor: 3.686

8.  From data point timelines to a well curated data set, data mining of experimental data and chemical structure data from scientific articles, problems and possible solutions.

Authors:  Villu Ruusmann; Uko Maran
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9.  Are bigger data sets better for machine learning? Fusing single-point and dual-event dose response data for Mycobacterium tuberculosis.

Authors:  Sean Ekins; Joel S Freundlich; Robert C Reynolds
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10.  Bigger data, collaborative tools and the future of predictive drug discovery.

Authors:  Sean Ekins; Alex M Clark; S Joshua Swamidass; Nadia Litterman; Antony J Williams
Journal:  J Comput Aided Mol Des       Date:  2014-06-19       Impact factor: 3.686

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