Grappa – a machine learned molecular mechanics force field

Abstract

Simulating large molecular systems over long timescales requires force fields that are both accurate and efficient. In recent years, E(3) equivariant neural networks have lifted the tension between computational efficiency and accuracy of force fields, but they are still several orders of magnitude more expensive than established molecular mechanics (MM) force fields. Here, we propose Grappa, a machine learning framework to predict MM parameters from the molecular graph, employing a graph attentional neural network and a transformer with symmetry-preserving positional encoding. The resulting Grappa force field outperforms tabulated and machine-learned MM force fields in terms of accuracy at the same computational efficiency and can be used in existing Molecular Dynamics (MD) engines like GROMACS and OpenMM. It predicts energies and forces of small molecules, peptides, and RNA at state-of-the-art MM accuracy, while also reproducing experimentally measured values for J-couplings. With its simple input features and high data-efficiency, Grappa is well suited for extensions to uncharted regions of chemical space, which we show on the example of peptide radicals. We demonstrate Grappa's transferability to macromolecules in MD simulations from a small fast-folding protein up to a whole virus particle. Our force field sets the stage for biomolecular simulations closer to chemical accuracy, but with the same computational cost as established protein force fields.