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

Interdisciplinary Research Areas
© O. Koch

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 interest lies in the development and application of computational methods in rational drug design with focus on structure-based design and ‘big data’ driven decisions in order to develop bioactive molecules and to understand selectivity, promiscuity and polypharmacology of protein-ligand interactions. This work is generally aimed at applying and improving the performance of computational methods in delivering novel and safe small molecule therapeutics. In an interdisciplinary way, the in-silico work is combined with biochemical evaluation and preparative organic synthesis for the identification and optimisation of promising molecules and the evaluation of newly developed methods (see figure). My development research projects are focused on (new) data-oriented methods and the application of artificial intelligence for the improvement of virtual screening approaches, and the analysis of protein-ligand interactions and the underlying framework of protein binding sites. The increase in the number of protein structures and the tremendous amounts of bioactivity data still need new tools and approaches or a better understanding of existing tools for efficient data mining and knowledge discovery. The aim is to use this knowledge, the available ‘big’ bioactivity data and protein structures for computational molecular design and identification of new bioactive compounds. A special focus is on the identification of promiscuous fragments that can generally be used for fragment-based design approaches.

On the following pages you can find more information about the group members and our research and scientific output.

Latest News

11/2022 Lecture in the context of the further education of the 'Apothekerkammer Westfalen Lippe'

Prof. Koch gives a talk "An introduction into artificial intelligence" on 20.11.2022 in the context of the further education of the 'Apothekerkammer Westfalen Lippe (AKWL)'. AKWL members are invited to join this lecture. This lecture will be provied a 2nd time on 22.03.2023.

10/2022: Teaching - An introduction into Artificial Intelligence (WS2022/2023, 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 was 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: EEIDKI-2022_2

10/2022: Teaching - Drug Design and Development (WS2022/2023, Teaching Language: German)

This course gives an introduction into modern drug design and development methods.
The link to the Learnweb course: VDDUE-2022_2

08/2022: Philipp Otten joins the group as a PhD student

After successfulling finishing his master thesis in our group, Philipp Otten will stay as a PhD student. He will focus on computational molecular design and synthesis of new thioredoxin reductase inhibitors.