Literature DB >> 16478689

Mining chemical structural information from the drug literature.

Debra L Banville1.   

Abstract

It is easier to find too many documents on a life science topic than to find the right information inside these documents. With the application of text data mining to biological documents, it is no surprise that researchers are starting to look at applications that mine out chemical information. The mining of chemical entities--names and structures--brings with it some unique challenges, which commercial and academic efforts are beginning to address. Ultimately, life science text data mining applications need to focus on the marriage of biological and chemical information.

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Year:  2006        PMID: 16478689     DOI: 10.1016/S1359-6446(05)03682-2

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


  14 in total

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Authors:  Kristina M Hettne; Antony J Williams; Erik M van Mulligen; Jos Kleinjans; Valery Tkachenko; Jan A Kors
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6.  Tunable machine vision-based strategy for automated annotation of chemical databases.

Authors:  Jungkap Park; Gus R Rosania; Kazuhiro Saitou
Journal:  J Chem Inf Model       Date:  2009-08       Impact factor: 4.956

7.  Manipulating In-House Designed Drug Databases For The Prediction of pH-Dependent Aqueous Drug Solubility.

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8.  Enhancement of chemical entity identification in text using semantic similarity validation.

Authors:  Tiago Grego; Francisco M Couto
Journal:  PLoS One       Date:  2013-05-02       Impact factor: 3.240

9.  The Text-mining based PubChem Bioassay neighboring analysis.

Authors:  Lianyi Han; Tugba O Suzek; Yanli Wang; Steve H Bryant
Journal:  BMC Bioinformatics       Date:  2010-11-08       Impact factor: 3.169

10.  Using workflows to explore and optimise named entity recognition for chemistry.

Authors:  Balakrishna Kolluru; Lezan Hawizy; Peter Murray-Rust; Junichi Tsujii; Sophia Ananiadou
Journal:  PLoS One       Date:  2011-05-25       Impact factor: 3.240

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