Prof. Dr. Oliver Koch
Heisenberg-Professor of Computational Drug Discovery
Institute of Pharmaceutical and Medicinal Chemistry
and German Center of Infection Research
Corrensstr. 48, 48149 Münster
Tel.: +49 (0)251 - 83-33443
Fax: +49 (0)251 - 83-32144
Keywords: medicinal chemistry, computational molecular design, cheminformatics, structure-based design, fragment-based design, artifical intelligence, data-driven decision making, design-synthesize-test cycle
The importance of computational methods in pharmaceutical drug research was recently highlighted in a special issue on computer-aided drug design (CADD) strategies in pharma (A CADD-alog of strategies in pharma). The identification and development of new drugs is nowadays hardly imaginable without the use of computational methods. These methods support the complete development workflow from initial target and hit identification up to the development of the final drug candidates.
My research work is generally aimed at developing new bioactive molecules and improving the performance of computational methods in delivering novel and safe small molecule therapeutics. One of the ways in which this is achieved is by gaining a better understanding of protein-ligand interactions. This includes the development of new computational methods, and the extension and application of already existing approaches. The computational work is combined with biochemical evaluation, X-ray crystallography and preparative organic synthesis of small molecular compounds for having the whole design-synthesize-test workflow in one group. The research projects can be summarized as structure-based design and data-driven decision making combined with artificial intelligence towards the development of new bioactive molecules.
On the following pages you can find more information about the group members and our research and scientific output.
04/2022: Looking for a new phd student - Cheminformatics and AI
This position is part of a third-party funded priority programm “Molecular Machine Learning” (https://www.uni-muenster.de/SPP2363/). The programme aims at connecting communities from the fields of machine learning and data science with scientists working in the areas of molecular chemistry and pharmacology. The specific project will continue our neural fingerprints approach as a new molecular representation suitable for virtual screening approaches (https://doi.org/10.1016/j.csbj.2021.07.032). The candidate will work on this project closely together with another PhD student from a computer science group in Münster.
Application deadline: 08 April 2022
Here is the link to the official job advertisment: Advertisment-Link
11/2021: Invited Talk at 'Discovery Chemistry US' conference
Dr. Koch gives a talk at the 'Discovery Chemistry US' confrence with the title "Neural Fingerprints: Generating Novel Domain-Specific Molecular Fingerprints Using Neural Networks"
09/2021: Teaching - An introduction into Artificial Intelligence (WS2021/2022, Teaching Language: German)
This course gives an introduction in into artifitical intelligence for pharmacists (and master chemistry and drug science). It combines a theoretical lecture (1 hour per semester) with practical excercise as jupyter notebooks (1 hour per semester). The development of this course is funded by the "Apothekerstiftung Westfalen Lippe)
The course will take place every friday, Seminarroom 1, Pharmacampus.
The theoretical lecture will take place from 10.00 c.t. to 11.00.
The practical excercise will take place from 11.00 ct. to 12.00. Please bring a laptop.
The link to the Learnweb course: Learnweb EIDKI-2021_2
09/2021: Teaching - Drug Design and Development (WS2021/2022, Teaching Language: German)
This course gives an introduction into modern drug design and development methods. Learnweb VDDUE-2021_2
08/2021 A new publication online: Natural Product Scores and Fingerprints Extracted from Artificial Neural Networks
Menke, J., Massa, J., Koch, O.* Natural Product Scores and Fingerprints Extracted from Artificial Neural Networks. Comput. Struct. Biotechnol. J., 2021; online available. https://doi.org/10.1016/j.csbj.2021.07.032