Following a holistic pharmacoinformatic approach we combine structural modeling of proteins, structure-based drug design, chemometric and in silico chemogenomic methods, statistical modeling and machine learning approaches to develop predictive computational systems for transporters and ion channels. The validation and optimisation of the obtained in silico models by strong links to experimental groups is an integral part of these activities. Furthermore, we aim at developing tools for in vitro to in vivo translation and for toxicity prediction, thereby leveraging integrated life science data as provided by the Open PHACTS Discovery Platform. Research activities are complemented by strong educational activities, outlined in the doctoral program MolTag and in EUROPIN, a European PhD Program in Pharmacoinformatics. The main scientific aims of the Research Group deal with the molecular basis of promiscuous drug-protein interaction (off-pharmacologies) and the development and use of complex, non-linear methods in the drug discovery process. Currently our targets include several ABC-transporter, the hERG potassium channel, as well as the GABAA receptor. On a methodological basis, we work on computational models for prediction of toxicity endpoints as well as development of KNIME workflows for complex data analysis.