
TYC Mini-Symposium Bio Interest Group: Chris Oostenbrink, BOKU & Franca Fraternali, UCL
24 April 2025 @ 3:00 pm – 6:00 pm
Drinks reception will be held in Physics E3/7 ground floor
Use of machine-learned potentials in QM/MM settings: the BuRNN methodology – Chris Oostenbrink
Institute for Molecular Modelling and Simulation, Department of Natural Sciences and Sustainable Resources, BOKU University, Vienna, Austria
In hybrid quantum mechanics / molecular mechanics (QM/MM) approaches, the molecular system is partitioned into regions that are treated at different levels of theory. At the interfaces between these regions, artifacts may occur. Examples are an overpolarization of the QM region due to near partial charges in the MM region, the lack of polarization in the MM region or unbalanced interactions between particles in the different regions, leading to an intrusion of MM particles into the QM region, or an accumulation or depletion of QM particles if particles are allowed to change character.
We have recently introduced a buffered embedding scheme, in which a buffer region between the inner (QM) and outer (MM) region is defined for which the interactions are computed both at the QM and MM level. This comes at the cost of introducing a second QM-calculation at every timestep of the simulation. The use of neural networks to describe molecular potential energies, allows for an elegant solution to this problem. We train a neural network directly on the difference between the two QM calculations, ensuring that the network reproduces the QM-interactions of the inner region, with itself and with the buffer region as well as the polarization of the buffer region due to the inner region. Any remaining artifacts largely cancel in the trained differences and are far removed from the inner region of interest. The use of the Buffer Region Neural Network (BuRNN) approach, furthermore, allows us to apply alchemical free-energy calculations at the QM-level of theory. In this presentation, I will demonstrate our most recent advances with BuRNN.
- Lier, B., Poliak, P., Marquetand, P., Westermayr, J., Oostenbrink, C. (2022) BuRNN: Buffer Region Neural Network Approach for Polarizable-Embedding Neural Network/Molecular Mechanics Simulations. J Phys Chem Lett 13, 3812-3818. doi: 10.1021/acs.jpclett.2c00654
- Crha,R., Poliak, P., Gillhofer, M., Oostenbrink C. (2025) Alchemical Free-Energy Calculations at Quantum-Chemical Precision. J. Phys. Chem. Lett 16, 863–869. doi: 10.1021/acs.jpclett.4c03213
ImmunoMatch, machine learning framework for deciphering the molecular rules governing the pairing of antibody chains – Franca Fraternali
University College London
ImmunoMatch facilitates the computational assessment and modelling of stably assembled immunoglobulins towards large-scale optimisation of efficacious antibody therapeutics.
Organised by:
Edina Rosta
e.rosta@ucl.ac.uk