Jensen Group - Computational Quantum Chemistry
The Jensen Group uses quantum chemistry and machine learning to discover new molecules with interesting properties and predict how they can be synthesised.
Research focus
The group uses quantum chemistry and machine learning to help discover new molecules with interesting properties and how to synthesise them.
Examples include work with AstraZeneca in Sweden on finding new catalysts with increased substrate scope and with Bayer AG in Germany on improving methods for predicting retrosynthetic routes for drug-like molecules.
The research also includes fundamental aspects of machine learning, such as explainable AI and uncertainty quantification.
The group’s work connects three areas:
- Molecule synthesis
Reaction prediction, retrosynthesis and reaction discovery. - Methods
Quantum chemistry, machine learning, genetic algorithms and docking. - Molecule discovery
Antibiotics, molecular batteries and catalysts.
Group leader
Jan Halborg Jensen
Professor
Research examples
The current research page highlights several areas of work:
- Antibiotic discovery
Work related to the Open Source Antibiotics project uses genetic algorithms and docking software to identify new antibiotic candidates. - Molecular batteries
Research with colleagues at the University of Copenhagen explores molecules that can store solar energy as chemical energy without thermal insulation. - Catalyst discovery
Projects combine genetic algorithms and machine learning tools to discover catalysts, including catalysts that can convert nitrogen in the atmosphere to fertiliser.
Collaborations
The group collaborates with academic and industrial partners, including:
- AstraZeneca
- Bayer AG
- Novozymes
- Novo Nordisk
- Department of Biology, University of Copenhagen