Molecular mechanics (MM) force fields -- the models that characterize the
energy landscape of molecular systems via simple pairwise and polynomial terms
-- have traditionally relied on human expert-curated, inflexible, and poorly
extensible discrete chemical parameter assignment rules, namely atom or valence
types. Recently, there has been significant interest in using graph neural
networks to replace this process, while enabling the parametrization scheme to
be learned in an end-to-end differentiable manner directly from quantum
chemical calculations or condensed-phase data. In this paper, we extend the
Espaloma end-to-end differentiable force field construction approach by
incorporating both energy and force fitting directly to quantum chemical data
into the training process. Building on the OpenMM SPICE dataset, we curate a
dataset containing chemical spaces highly relevant to the broad interest of
biomolecular modeling, covering small molecules, proteins, and RNA. The
resulting force field, espaloma 0.3.0, self-consistently parametrizes these
diverse biomolecular species, accurately predicts quantum chemical energies and
forces, and maintains stable quantum chemical energy-minimized geometries.
Surprisingly, this simple approach produces highly accurate protein-ligand
binding free energies when self-consistently parametrizing protein and ligand.
This approach -- capable of fitting new force fields to large quantum chemical
datasets in one GPU-day -- shows significant promise as a path forward for
building systematically more accurate force fields that can be easily extended
to new chemical domains of interest