Floriane holds a bachelor’s degree in life science and bioinformatics and a master’s degree in bioinformatics and in Silico drug design from the University of Paris. She joined our lab in February 2023 as a PhD candidate to be part of the BMBF-funded MORPHEUS project. She will work on the development of deep learning models to predict the effects of substances and identify characteristic fingerprints based on morphological (cell painting) and molecular input data.
Take a look at our newest opinion paper Machine learning for small molecule drug discovery in academia and industry.
Great collaboration with academic and industry colleagues discussing advances and challenges in molecular machine learning. Despite common overarching goals, we highlight the differences between academic and industry to improve models for e.g. drug selection and share ideas to improve collaborations.
Thanks to all co-authors for working together on this project with such enthusiasm!
Paula completed her Bioinformatics bachelor’s and master’s at Saarland University. She is joining Volkamer lab as a PhD candidate as part of the NextAID research group, where the focus is on deep learning for drug discovery. She will be working on combining deep learning with fragment based drug design to generate novel ligand candidates for kinase inhibition.
Michael obtained his PhD under the supervision of Prof. Dr. Verena Wolf. As part of the NextAID team his work focuses on the development of machine learning models for drug discovery.
We are happy to announce that the lab moved to Saarland University, where we focus on Data Driven Drug Design! We are embedded in a vibrant environment, located in the Center for Bioinformatics and being part of Saarland Informatics Campus as well as the HIPS.
We are thankful for the time at Charité Berlin and are looking forward to many interesting projects and collaborations in Saarbrücken.
Struc2Drug The latest iteration of Struc2Drug, will be held on Thu 07. April 2022, 4pm - 5pm CET by the Volkamer Lab. Struc2drug is a bimonthly seminar series promoting the exchange between researchers in the interdisciplinary field of structural biology and drug development in Berlin.
Confirmed speakers for the next series include:
Dr. Mikhail Kudryashev (Kudryashev lab, Max Delbrück Center for Molecular Medicine, Berlin) “Activation of the serotonin receptor ion channel 5-HT3 probed by cryo-EM” Vasilii Mikirtumov (Kudryashev lab, Max Delbrück Center for Molecular Medicine, Berlin) “Structure and activation mechanism of ryanodine receptor isoform 1 in native membrane” No registration is needed, please join the meeting via FU berlin at https://fu-berlin.
The KLIFS database is a rich resource for datasets focused on kinase pockets, ranging from annotated pockets and ligand interaction patterns in experimental PDB structures to ligand bioactivity values from the ChEMBL database.
It is possible to explore the KLIFS resource via their web interface online (NAR 2021) or locally using dedicated KNIME nodes (JCIM 2017 and ChemMedChem 2018 developed by the KLIFS authors.
With OpenCADD-KLIFS, we now add a Python solution for easy and quick integration of KLIFS datasets into Python-based pipelines.
Digidrug First up, on Thu 17. February 2022, 4pm - 5:30pm CET the next episode of the virtual seminar series, Digital Science for Drug Discovery will be held.
Focused on the Berlin region, the series aims to facilitate and enmesh relationships between researchers working within both academia and industry. The over-arching theme? Making efficient and creative use of the wealth of available and growing chemical and biological data combined with powerful computational means at our disposal.
Kinases are an important class of drug targets since their dysregulation can cause severe diseases such as cancer, inflammation, and neurodegeneration. Finding selective drugs, however, is challenging due to the highly conserved binding sites across the roughly 500 human kinases, which can lead to unwanted side-effects. The underlying off-targets are often not trivial to predict or to explain from a sequence-based perspective.
In our KiSSim project, we explored kinase similarity from a physicochemical and structural point of view.
Assessing kinase similarity, or how to avoid side effects? Kinases are highly conserved in their binding site, which presents a challenge since one ligand may bind not only the designated target (on-target) but also other targets (off-targets), causing mild to severe sides effects. Being able to assess kinase similarity could therefore give insight into potential side-effects.
The proposed pipeline, part of the TeachOpenCADD project, allows the study of kinase similarities from four different angles in an automated and modular fashion.