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“Development of ADME/T methods using Associative Neural Networks (ASNN) - A novel self-learning software for confident ADME/T predictions” (BMBF, Germany, 2007-2010). This project has one of its goals to develop methodology for estimation of the applicability domain of models, the accuracy of prediction and to increase the performance of methods by data integration. Preliminary results have demonstrated the feasibility and high accuracy of this methodology.

Collaboration with ECO: Application of these methods to REACH problems taking into account chemical categories and grouping as well as chemical speciation.

Partners: HMGU

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