De novo drug design
Our methodological repertoire includes ligand- and receptor-based techniques, and AI methods that consider multiple properties in parallel, including the synthetic feasibility of the computer-generated molecules and their polypharmacology. The aim is to provide surprising, innovative ideas for the discovery of new chemical entities.
Often starting from known drugs or pharmaceutically active natural products as templates, our algorithms suggest drug-like molecules with motivated scaffold variations Advanced mathematical models of structure-activity landscapes and multi-objective design techniques create new opportunities for hit and lead finding.
Nature-inspired molecular design and optimization
The search for new compounds by in silico compound assembly requires smart navigation in "chemical space". We develop algorithms that are inspired by problem-solving strategies from nature, for example the path-finding abilities of certain ant species or swarm behavior, and adapt these concepts to chemistry. We also employ game theory and related approaches from the field of artificial intelligence.
From models to molecules by microfluidics-assisted synthesis
Continuous flow reactors are increasingly used in synthetic organic chemistry. This enabling technology to access innovative chemotypes and
efficiently explore chemical space in an automated manner is being adapted for chemical biology and drug discovery research. The tight integration of microfluidics-assisted synthesis with computer-based target prediction represents a viable approach to rapidly generate bioactivity-focused combinatorial compound libraries with high success rates. We explore the possibilities and limitations of this innovative molecular design concept.
Anticancer and antiinfective drug discovery
Other relevant targets for our drug discovery efforts are G-protein coupled receptors (GPCRs) and kinases. We have established a multi-objective molecular design approach that considers multiple desirable properties of computer-generated compounds, such as synthesizability and target-panel selectivity. Structure-activity landscapes guide compound optimization and prioritization.
