  The development of reliable and extensible molecular mechanics (MM) force
fields -- fast, empirical models characterizing the potential energy surface of
molecular systems -- is indispensable for biomolecular simulation and
computer-aided drug design. Here, we introduce a generalized and extensible
machine-learned MM force field, \texttt{espaloma-0.3}, and an end-to-end
differentiable framework using graph neural networks to overcome the
limitations of traditional rule-based methods. Trained in a single GPU-day to
fit a large and diverse quantum chemical dataset of over 1.1M energy and force
calculations, \texttt{espaloma-0.3} reproduces quantum chemical energetic
properties of chemical domains highly relevant to drug discovery, including
small molecules, peptides, and nucleic acids. Moreover, this force field
maintains the quantum chemical energy-minimized geometries of small molecules
and preserves the condensed phase properties of peptides, self-consistently
parametrizing proteins and ligands to produce stable simulations leading to
highly accurate predictions of binding free energies. This methodology
demonstrates significant promise as a path forward for systematically building
more accurate force fields that are easily extensible to new chemical domains
of interest.
