This website may no longer hold accurate information, please contact Barbara van Kampen for information. The new website is under construction (March 30 2018) 


The Computational Discovery and Design (CDD) Group was established at the Center of Molecular and Biomolecular Informatics (CMBI) of the Radboud University of Nijmegen in 2003 by part-time professor Jacob de Vlieg. Key goal of the CDD group is to develop innovative molecular modeling and computer-based simulation techniques for structure-based drug design, translational medicine and protein family based approaches to design and identify drug-like compounds.


The CDD group is working closely together with the Department of Molecular Design & Informatics (MDI) of Schering-Plough, resulting in a unique and fruitful collaboration between academic research (i.e. University via CDD/CMBI) and applied research (Pharma Industry via MDI). CDD is focusing on new scientific approaches, while MDI is engaged in defining and illustrating real life problems and validating new in silico methods in drug discovery projects (so called “wet-dry” validation).


Computational drug discovery has created many opportunities to accelerate and rationalize the multidisciplinary drug discovery process, and provide novel approaches to the design of drugs. Today, in-silico drug hunters must work across many disciplines ranging from molecular biology to chemistry and physics in order to translate the vast amounts of information from protein targets, ligands and their complexes into useful knowledge. A variety of computational scientific techniques are applied to solve complex chemical and biological problems including micro array analysis, statistical analysis of large datasets, structure-based drug design, computational genomics, molecular simulations and pharmacophore based molecular library design.


Computational drug discovery plays a critical role in catalyzing the intensive "wet-dry" cycle that characterizes modern drug design. An important goal is to develop integrative molecular informatics techniques to design compounds while simultaneously optimizing potency, selectivity, efficacy and ADMET properties.