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Chem Rev. 2017 Jun 28;117(12):7673-7761. doi: 10.1021/acs.chemrev.6b00851. Epub 2017 May 5.

Information Retrieval and Text Mining Technologies for Chemistry.

Author information

1
Structural Computational Biology Group, Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre , C/Melchor Fernández Almagro 3, Madrid E-28029, Spain.
2
Small Molecule Discovery Platform, Molecular Therapeutics Program, Center for Applied Medical Research (CIMA), University of Navarra , Avenida Pio XII 55, Pamplona E-31008, Spain.
3
ESEI - Department of Computer Science, University of Vigo , Edificio Politécnico, Campus Universitario As Lagoas s/n, Ourense E-32004, Spain.
4
Centro de Investigaciones Biomédicas (Centro Singular de Investigación de Galicia) , Campus Universitario Lagoas-Marcosende, Vigo E-36310, Spain.
5
CEB-Centre of Biological Engineering, University of Minho , Campus de Gualtar, Braga 4710-057, Portugal.
6
Life Science Department, Barcelona Supercomputing Centre (BSC-CNS) , C/Jordi Girona, 29-31, Barcelona E-08034, Spain.
7
Joint BSC-IRB-CRG Program in Computational Biology, Parc Científic de Barcelona , C/ Baldiri Reixac 10, Barcelona E-08028, Spain.
8
Institució Catalana de Recerca i Estudis Avançats (ICREA) , Passeig de Lluís Companys 23, Barcelona E-08010, Spain.

Abstract

Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.

PMID:
28475312
DOI:
10.1021/acs.chemrev.6b00851
[Indexed for MEDLINE]

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