Casper Steinmann

Casper Steinmann is an Associate Professor in the Department of Chemistry and Bioscience at Aalborg University's Faculty of Engineering and Science. His research is centered on computational and applied supramolecular chemistry, with a focus on molecular modeling, drug design, and chemical informatics. He leads several research projects integrating machine learning and robotics in chemical synthesis.

His primary research interests include computational chemistry, supramolecular interactions, cyclodextrin-based systems, docking simulations, and the development of interoperable cheminformatics tools. He is particularly active in structure-guided drug discovery and the prediction of physico-chemical properties of complex molecular systems.

The recent publications highlight a strong trend in computational drug discovery, targeting viral proteins like SARS-CoV-2 helicase and neurodegenerative disease targets such as LRRK2. His work also spans materials science, particularly in nanoparticle-based antimicrobial therapies, and software development for computational chemistry, exemplified by the cclib 2.0 project. These reflect interdisciplinary research bridging chemistry, biology, and computer science.

• Årets Underviser på Institut for Kemi og Biovidenskab (2022)
• Årets Underviser på Ingeniør- og Naturvidenskabelige Fakultet (2022)

Casper Steinmann has been actively involved in research supervision and external examination of PhD theses, indicating a strong commitment to academic mentoring. He has secured multiple research grants, including funding from the Villum Foundation and Danish e-infrastructure Cooperation, supporting projects in automated synthesis, machine learning in chemistry, and molecular property prediction. His activities include extensive peer review, conference participation, and industry talks.

He is involved in projects such as 'Designing Molecules for Ease of Chemical Synthesis' and 'Prediction of physico-chemical properties of guest cyclodextrin inclusion complexes,' often in collaborative networks. His team engages with robotics in chemical synthesis and develops computational frameworks for polarizable embedding and molecular simulations.