Theoretical tools
The basic tools used during the project and schools will include linear and non-linear methods of data analysis, including both supervised (linear regression, Support Vector Machines, neural networks) and unsupervised (self-organizing neural networks, hierarchical clustering analysis, etc.) approaches developed by the participants (HMGU, UMB) and available at the Virtual Computational Chemistry Laboratory site (VCCLAB), as well as those available within the OECD QSAR toolbox, open source software tools like WEKA, R-language, and AMBIT. The optimization of molecular structures will be performed using semi-empirical quantum-mechanical program AM1 from the MOPAC package and on-line software tools available at the VCCLAB site. Importantly, TALETE agreed to provide time limited free licenses of Dragon software to all students for their training during the Schools. The remote version of this tool, E-Dragon, available at the VCCLAB site will be used by the students for the development of models for their works on the project. All ESR will have personal computers and qualified support from system administrators within each group to perform their studies.
Experimental tools and resources
The HMGU, RIVM, INIA partners of the project are the centers of excellence within their respective countries. This allows for access to the most advanced and unique instruments available in their countries and in Europe. For example, HMGU hosts the most powerful Fourier-transform-ion cyclotron resonance mass spectrometer (FT-ICR MS) in Europe for ultrahigh-resolution analysis and detection of compounds and their metabolites. The scientists of HMGU have access (following a formal application) to the German Leibniz-Rechenzentrum national supercomputing center, which is ranked 15th in the world. It can be used for any time-demanding calculations (e.g., ab initio quantum chemistry). All partners have all necessary equipment to run experiments for the tasks described in the proposal.
Collaborations making use of these complementary team competencies already exist between multiple network participants, as indicated on the Figure and will be fostered during the ECO.