Volkamer Lab
In silico toxicology and structural bioinformatics
At Volkamer Lab, we develop methods at the interface between structural bioinformatics and cheminformatics, mostly applied in the context of computer-aided drug design.
On one hand, we investigate structure-based methods for active site assessment, including binding site comparison, pharmacophore elucidation and off-target prediction; pocket-centric fragment-based design as well as deep learning enhanced virtual screening pipelines. Besides target-independent approaches, we focus our developments on kinases, one of the major classes of therapeutic targets.
On the other hand, we study ligand-based methods, including machine learning methods for activity and toxicity prediction and apply them in translational projects. Special focus is set on integrating novel techniques taking the applicability and reliability into account, and the interpretability of deep learning methods. Our major goal here is the establishment of alternative in silico methods to determine the risk of compounds and their harmful effects on humans, animals, plants and environment (see BB3R platform).
Additionally, we continuously work on the integration of the two research areas of structure- and ligand-centered projects, i.e., focusing on structure-informed machine learning approaches applied primarily to kinases. In this attempt, we are integrating the physical aspects into molecular modelling (e.g. molecular dynamics simulations and free energy calculations) to better sample the drug target interactions. The OpenKinome initiative is an ongoing collaboration with the lab of Prof. John Chodera, our Einstein BIH Visiting Fellow.
Latest news
- 2021.08.10: SAVE THE DATE 09.09.21 - Digital Science for Drug Discovery
- 2021.04.23: Deep learning in virtual screening, what's new?
- 2021.01.08: FU Softwarepraktikum for 2021: structural alignment
- 2020.11.06: Read about KinFragLib in JCIM
- 2020.08.24: KinFragLib is online!
- 2020.07.09: Struc2drug meetings are back
- 2020.06.18: We Are Online!
- 2020.04.13: Join us in our collective effort to better understand CoViD-19
