Team Volkamer - in silico toxicology and structural bioinformatics

The group of JProf. Volkamer is working on novel methods for in-silico toxicology predictions.

You are here:

The project is part of the BMBF-funded BB3R initiative. One major goal of the BB3R initiative is the establishment of alternative methods for preclinical drug development and basic research. To assess the toxicity of novel chemical entities, regulatory agencies require in-vivo testing for several toxic endpoints. In 2010, roughly 2.9 million laboratory animals have been deployed in Germany, with an increase of 6% since 2008. Thus, the establishment of alternative methods, and with it the reduction of animal testing, is of utmost importance. Determining the toxicity of compounds is vital to identify their harmful effects on humans, animals, plants and the environment.

The focus of the AG Volkamer is the development of structure-based methods to come closer to the vision of transforming toxicology into a predictive science and reducing the number of animal testing. The group strongly focuses on computer-aided prediction of off-target effects as well as the identification of novel 'toxicophores' and toxicity targets (targets associated with adverse drug reactions) using ligand- as well as protein-based structural and physicochemical information. 

Structure-based (binding site assessment and comparison, pharmacophore elucidation, structure-function relationship) and ligand-based (screening, QSAR, machine learning) methods are investigated to guide the design of more selective and less toxic compounds.

The group is looking forward to new challenges and new collaborations with computational and experimental partners to jointly apply the novel methods to acute questions.

Publications / Awards

  • Sydow, Morger, Driller, Volkamer, (2019), “TeachOpenCADD: A teaching platform for computer-aided drug design using open source packages and data.” Journal of Cheminformatics, 11(1), 29.
  • Sydow, Burggraaff, Szengel, van Vlijmen, IJzerman, van Westen, Volkamer, (2019), "Advances and Challenges in Computational Target Prediction", Journal of Chemical Information and Modeling, epub.
  • Schmitt, Gosch, Dittmer, Rothemund, Mueller, Schobert, Biersack, Volkamer, Höpfner, (2019), Oxazole-bridged combretastatin A-4 derivatives with tethered hydroxamic acids: Structure-activity relations of new inhibitors of HDAC and/or tubulin function, International Journal of Molecular Sciences, 20(2), 383.
  • Mortier, Dhakal, Volkamer, (2018). Truly Target-Focused Pharmacophore Modeling: A Novel Tool for Mapping Intermolecular Surfaces, Molecules, 23(8), 1959.
  • Volkamer, Riniker, (2018). Transition from Academia to Industry and Back, J. Chem. Inf. Model.
  • Volkamer, et al., (2018). Prediction, Analysis and Comparison of Active Sites; Book chapter in Cheminformatics, Basic Concepts and methods (Wiley), ed. Engel, Gasteiger.
  • Lang, Volkamer, et al., (2018). In silico Methods – Computational Alternatives to Animal Testing. ALTEX - Alternatives to animal experimentation. 35(1): 126-128.
  • Kooistra, Volkamer, (2017). Kinase-centric computational drug development. Book chapter: Annual Reports in Medicinal Chemistry (Elsevier): Platform Technologies in Drug Discovery and Target Validation, Volume 50, 197-236.
  • Bietz, et al., Volkamer, Rarey, (2017). From cheminformatics to structure-based design: Web services and desktop applications based on the NAOMI library. J.Biotec., 261: 207-214.
  • Fährrolfes, et al., Volkamer, Rarey, (2017). ProteinsPlus: a web portal for structure analysis of macromolecules. Nucleic Acid Res, 45(W1): W337-W343.
  • Eid, Turk, Volkamer, Rippmann, Fulle, (2017). KinMap: a web-based tool for interactive navigation through human kinome data. BMC Bioinformatics, 18:16.

Team-leader

Students/Interns

Boran Adas

Master student

Konterfei_Mann_155x200.jpg
Paula Junge

Master student

paula_155x200.jpg

Alumni

  • Henry Webel (Scientist): Predicting cytotoxicity using deep neural networks
  • Dr. Jérémie Mortier (Scientist): Truly target-focused pharmacophore modeling (T2F-Pharm)
  • Shalini Muralikumar (Intern): In silico investigation of protein-protein interactions during sumoylation of Smyd1
  • Pratik Dhakal (Student assistant + master thesis): Truly target-focused (dynamic) pharmacophore modeling (T2F-Pharm and T2F-Flex)
  • Eva Aßmann (Bachelor thesis): Predicting kinase similarity using a novel fingerprint-based binding site comparison method
  • Jacob Gora (Student assistant): Machine learning for kinase activity prediction
  • Jacob Gora (Master thesis with Novartis): Active learning for compound optimization