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Welcome to the OpenTox predictive toxicology framework. You can find a project description here and more details on development at the OpenTox Wiki.
User Survey and Input
We have a strong belief that users and their needs should drive the directions of OpenTox development and that we should respond to their needs. Please take a few minutes to complete the following user feedback form which is available as a web survey and we will endeavour to respond to your input:
OpenTox - CADASTER User Survey
Description of Project
The goal of the OpenTox project is to develop a predictive toxicology framework, that provides a unified access to toxicological data, (Q)SAR models and toxicological information.
The OpenTox framework will provide tools for the integration of data from various sources (public and confidential), for the generation and validation of (Q)SAR models for toxic effects, libraries for the development and seamless integration of new (Q)SAR algorithms, and scientifically sound validation routines. OpenTox will attract users from a variety of research areas:
• Toxicological and chemical experts (e.g. risk assessors, drug designers, researchers)
• (Q)SAR model developers and algorithm developers
• Non-(Q)SAR specialists requiring access to Predictive Toxicology models and data
The OpenTox project will move beyond existing attempts to solve individual research issues within this area, by providing a flexible, extensible, and user friendly framework that integrates existing solutions as well as providing easy access to new developments.
OpenTox will be relevant for the implementation of REACH as it allows regulatory and industrial risk assessors to access experimental data, (Q)SAR models and toxicological information from a unified, simple-to-use interface, that adheres to European and international regulatory requirements (e.g. OECD Guidelines for (Q)SAR validation, QSAR Model Reporting Formats (QMRF)). For maximum transparency OpenTox will be published as an open source project. This will allow a critical evaluation of the implemented algorithms, ensure a widespread dissemination and will attract external developers. Facilities for the inclusion of confidential in-house data and for accessing and integrating commercial prediction systems will be included.
The OpenTox framework will be populated initially with high-quality data and (Q)SAR models for chronic, genotoxic and carcinogenic effects. These are the endpoints, where computational methods promise the greatest potential reduction in animal testing, that would be required for the implementation of REACH. The impact of OpenTox will however go beyond REACH, industrial chemicals and long-term effects, because reliable toxicity estimates are also needed for other products (e.g., pharmaceuticals, cosmetics, food-additives) and endpoints (e.g,. sensitisation, liver-toxicity, cardio-toxicity).
The proposed framework will actively support the development of new (Q)SAR models by automating routine tasks, providing a testing and validation environment and allowing the easy addition of new data. It will also support the development of new algorithms and avoid duplicated work by providing easy access to common components, validation routines and an easy comparison with benchmark techniques. For this reason we expect, that OpenTox will lead to (Q)SAR models for further toxic endpoints and generally improve the acceptance and reliability of (Q)SAR models.
Project Partners
Douglas Connect, In Silico Toxicology, Ideaconsult, Istituto Superiore di Sanita', Technical University of Munich, Albert Ludwigs University Freiburg, National Technical University of Athens, David Gallagher, Institute of Biomedical Chemistry of the Russian Academy of Medical Sciences, Seascape Learning and the Fraunhofer Institute for Toxicology & Experimental Medicine
Advisory Board
European Center for the Validation of Alternative Methods, European Chemicals Bureau, U.S Environmental Protection Agency, U.S. Food & Drug Administration, Nestle, Roche, AstraZeneca, LHASA, University North Carolina, EC Environment Directorate General, Organisation for Economic Co-operation & Development, CADASTER and Bayer Healthcare
OpenTox - An Open Source Predictive Toxicology Framework, is funded under the EU Seventh Framework Program: HEALTH-2007-1.3-3 Promotion, development, validation, acceptance and implementation of QSARs (Quantitative Structure-Activity Relationships) for toxicology, Project Reference Number Health-F5-2008-200787 (2008-2011).
For further information on OpenTox programs or to discuss opportunities and potential for collaboration or your requirements as a user, please contact the project coordinator Dr. Barry Hardy at: barry.hardy [at] douglasconnect.com, Tel: +41 61 851 0170.
Latest news
OpenTox Development:
Communities and Collaboration in Discovery and Development
The following article including discussion of OpenTox and Synergy (http://www.synergy-ist.eu/) collaborative research has just been published:
Growing Significance of Communities and Collaboration in Discovery and Development
Barry Hardy
OpenTox Development:
Large-Scale Graph Mining Using Backbone Refinement Classes
The paper “Large-Scale Graph Mining Using Backbone Refinement Classes” by AM, SK and CH was presented at:
KDD 2009:
http://www.sigkdd.org/kdd2009/papers.html
OpenTox Development:
OpenTox Participation in CMTPI 2009
OpenTox partner Presentations at the conference "Fifth International Symposium on Computational Methods in Toxicology and Pharmacology Integrating Internet Resources" (CMTPI 2009, Istanbul, 04 - 08 July 2009
OpenTox Development:
OpenTox - Initial Analysis of ToxCast Phase 1 data
Collaboration of OpenTox with US EPA ToxCast program - initial analysis
OpenTox Development:
OpenTox and CADASTER Collaboration
About the OpenTox and CADASTER Collaboration
Latest projects
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LibFminer (11/05/2008 12:09 PM)
LibFminer
This is the Fminer library, available from http://github.com/amaunz/libfminer/tree/master. API documentation is available from http://www.maunz.de/libfminer-doc/. The documentation gives example programs, explains constructor usage and options.
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gSpan' (10/02/2008 11:47 AM)
gSpan'
C implementation of a graph mining algorithm
- feature generation: Mining for frequent subgraphs or subpaths/subtreesMore detail:
http://wwwkramer.in.tum.de/research/data_mining/pattern_mining/graph_mining -
FreeTreeMiner (10/02/2008 11:46 AM)
The FreeTreeMiner (FTM) software computes all subtrees (substructures) occuring at a given minimum frequency in a set of molecules. The subtrees are built via a depth first search (DFS). Additionally to the minimum frequency support, a maximum frequency constraint can be set. This constraint can either refer to the same database/set or to a second one, meaning that all subtrees frequent in the first and infrequent in the second one are returned by FTM.
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RUMBLE (10/02/2008 11:45 AM)
RUMBLE (RUle and Margin Based LEarner) is a statistically motivated rule learning system based on the Margin Minus Variance (MMV) approach. It is set up very flexible as it can make use of different plugins (e.g. FTM plugin, PROLOG plugin, Meta plugin) for different kinds of rules. Therefore RUMBLE can handle various different kinds of data.
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iSAR (10/02/2008 11:41 AM)
iSAR
Perl implementation of a lazy SAR algorithm
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