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Molecular Informatics
Review

The eTOX Library of Public Resources for in Silico Toxicity Prediction

M. Cases

Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Dep. of Experimental and Health Sciences, Universitat Pompeu Fabra, C/ Dr. Aiguader 88, Barcelona, Spain phone/fax: + 34 933 160 524/ + 34 933 160 550

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M. Pastor

Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Dep. of Experimental and Health Sciences, Universitat Pompeu Fabra, C/ Dr. Aiguader 88, Barcelona, Spain phone/fax: + 34 933 160 524/ + 34 933 160 550

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F. Sanz

Corresponding Author

E-mail address: fsanz@imim.es

Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Dep. of Experimental and Health Sciences, Universitat Pompeu Fabra, C/ Dr. Aiguader 88, Barcelona, Spain phone/fax: + 34 933 160 524/ + 34 933 160 550

Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Dep. of Experimental and Health Sciences, Universitat Pompeu Fabra, C/ Dr. Aiguader 88, Barcelona, Spain phone/fax: + 34 933 160 524/ + 34 933 160 550
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First published: 11 January 2013
Cited by: 5
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Abstract

(1000–1500 characters) In spite of the increasing amount of public access resources that offer original data related to drug toxicology, the successful exploitation of such data for the development of in silico predictive models is still limited by the quality of the data available, its integrability and its coverage for each toxicity endpoint. This work describes the strategy developed by the IMI eTOX consortium for identifying and compiling data and other related resources from the biomedical literature and a wide spectrum of public on‐line sources. The main result of this effort is a large web‐based structured library containing links to articles of toxicological relevance (data that can be used for modeling purposes, computational models, and toxicity mechanisms), public databases, standardized vocabularies and modeling tools. All this material has been manually reviewed, systematically evaluated and grouped into different categories. The library has been made public at the eTOX website (http://www.etoxproject.eu/), where it is updated on a monthly basis, constituting a useful resource for affording the in silico toxicity prediction of novel drug candidates.

Number of times cited according to CrossRef: 5

  • , Refinement, Reduction, and Replacement of Animal Toxicity Tests by Computational Methods, ILAR Journal, 57, 2, (226), (2016).
  • , Medicinal Chemistry Strategies to Prevent Compound Attrition, Attrition in the Pharmaceutical Industry, (215-228), (2015).
  • , The eTOX Data-Sharing Project to Advance in Silico Drug-Induced Toxicity Prediction, International Journal of Molecular Sciences, 15, 12, (21136), (2014).
  • , Predicting Toxic Effects of Metabolites, Drug Metabolism Prediction, (397-412), (2014).
  • , THE INNOVATIVE MEDICINES INITIATIVE: A PUBLIC PRIVATE PARTNERSHIP MODEL TO FOSTER DRUG DISCOVERY, Computational and Structural Biotechnology Journal, 10.5936/csbj.201303017, 6, 7, (e201303017), (2013).