Group photo taken in the medical plant garden, July 2022. Back row, left to right: Lennart Kinzel, André Asanoski, Katharina Beier, Jannis Wowra, Thomas-Martin Dutschmann. Front row, left to right: Dr. Nabiollah Mobaraki, Marc Hoffstedt, Marvin Stark, Prof. Dr. Knut Baumann.
Pre-clinical drug discovery and development generates a huge amount of data, which cannot be evaluated manually. Chemical structure and biological activity data from high-throughput screenings for drug discovery, the multitude of biological and toxicological assay data produced during drug development as well as analytical data from quality control and in vivo testing need computational techniques to turn the data into information. For instance, it would be valuable for the evaluation of series of activity data to relate those data to structural changes of the respective molecules to derive so-called structure-activity relationships. Machine learning provides the necessary tools to extract this information from the data. Prerequisites are that the chemical structures can be organized and processed efficiently. If this is the case, it is possible to detect patterns in which way structural changes affect bioactivity. This information can then be used to design novel molecules with improved properties. Chemoinformatics is the scientific discipline that deals with the efficient data handling and data evaluation of the chemical data space. Its focus is on preclinical drug discovery and drug development and represents our main research area.