Grappa -- A Machine Learned Molecular Mechanics Force Field
arXiv:2404.00050 · doi:10.1039/D4SC05465B
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 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.
References in corpus (11)
- Canonical sampling through velocity-rescaling
- Deep Residual Learning for Image Recognition
- E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
- Development of Novel Density Functionals for Thermochemical Kinetics
- Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks
- End-to-End Differentiable Molecular Mechanics Force Field Construction
- Mechanoradicals in tensed tendon collagen as a new source of oxidative stress
- Sobolev Training for Neural Networks
- Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond
- EspalomaCharge: Machine learning-enabled ultra-fast partial charge assignment
- On the Expressivity Role of LayerNorm in Transformers' Attention