Mission Statement

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 tools for drug discovery and development. Hereby, the main focus of our work comprise interaction of ligands with transmembrane transport proteins and prediction of toxicity. 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 in the area of adverse drug event prediction, thereby leveraging integrated life science data. Research activities are complemented by strong educational activities, outlined in the doctoral program MolTag and in EUROPIN, a European PhD Program in Pharmacoinformatics.

Latest News

Open Access
 

Are you curious what is known about SLCs? And how they are related? We are enthusiastic about our manuscript presenting the data- and...

News
 

Tarik Ćerimagić successfully defended his master thesis: "A Multi-Task Deep Neural Network Approach for Data Imputation of SLC Transporter...

News
 

On July 10th, 2024 our colleague Aljoša successfully defended his PhD thesis: "Machine Learning Approaches for Off-Target and Bioactivity...

Open Access
 

A big applaus goes to our Post Doc Sergey Sosnin for his open access publication on the development of a smart software tool for chemical risk...

Open Access
 

Proteochemometric modeling (PCM) combines ligand information as well as target information in order to predict an output variable of interest (e.g....

Open Access
 

Our new open access publication on ProteoMutaMetrics is out now! This work was performed within the REsolution project and was also supported by the...