title: The eTOX Library of Public Resources for in Silico Toxicity Prediction.
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.
authors.affiliation: 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.
authors.forename: M
authors.initials: M
authors.lastname: Cases
authors.affiliation: 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.
authors.forename: M
authors.initials: M
authors.lastname: Pastor
authors.affiliation: 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. fsanz@imim.es.
authors.forename: F
authors.initials: F
authors.lastname: Sanz
keywords: Computational models; Data quality; Predictive models; Public resources; Toxicology data