Chris Oostenbrink

Forschungsschwerpunkt

Our research focuses on the use of molecular dynamics simulations to describe molecular systems. Two current examples revolve around (i) the calculation of thermodynamics properties, like the free energy and (ii) the use of machine-learned potentials to improve the description of molecular interactions.

All chemical equilibria are governed by the difference in free energy between various states. Examples are free-energy differences between different conformations of a biomolecule or the free-energy difference between the bound and unbound state of a ligand and its pharmaceutical target. In our group we develop methods to efficiently and accurately calculate free-energy differences for such processes, using statistical mechanics and molecular simulation. In recent years, we have developed the accelerated enveloping distribution sampling (A-EDS) approach to calculate relative binding free energies for series of compounds.

Molecular interactions are governed by electronic degrees of freedom that are best described by quantum mechanics. However, the accurate calculation of the quantum-mechanical energies is computationally expensive. The use of hybrid QM/MM methods in which only a part of the molecular system is computed with quantum chemistry (QM), while the rest is described by molecular mechanics (MM) offers an interesting way to use the most accurate methods where it is needed. We recently developed an approach that leverages the use of machine-learned potentials to improve the description of QM/MM interactions, called BuRNN (Buffer Region Neural Networks).