  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
outperformstabulated 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, RNA and - showcasing its extensibility to
uncharted regions of chemical space - radicals at state-of-the-art MM accuracy.
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.
