Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond

Molecular mechanics (MM) force fields -- fast, empirical models characterizing the potential energy surface of molecular systems via simple parametric pairwise and valence interactions -- have traditionally relied on labor-intensive, inflexible, and poorly extensible discrete chemical parameter assignment rules using look-up tables for discrete atom or interaction types. Here, we introduce a machine-learned MM force field, espaloma-0.3, where the rule-based discrete atom-typing schemes are replaced with a continuous atom representations using graph neural networks. Trained in an end-to-end differentiable manner directly from a large, diverse quantum chemical dataset of over 1.1M energy and force calculations, espaloma-0.3 covers chemical spaces highly relevant to the broad interest in biomolecular modeling, including small molecules, proteins, and RNA. We show that espaloma-0.3 accurately predicts quantum chemical energies and forces while maintaining stable quantum chemical energy-minimized geometries. It can self-consistently parameterize both protein and ligand, producing highly accurate protein-ligand binding free energy predictions. Capable of fitting new force fields to large quantum chemical datasets with a single GPU-day of training, this approach demonstrates significant promise as a path forward for building systematically more accurate force fields that can be easily extended to new chemical domains of interest. The espaloma-0.3 force field is available for use directly or within OpenMM via the open-source Espaloma package https://github.com/choderalab/espaloma, and both the code and datasets for constructing this force field are openly available https://github.com/choderalab/refit-espaloma.