Brent Westbrook

We report a new charge model and a new general small molecule force field. Here, we address the development and benchmarking of both the Open Force Field (OpenFF) AshGC charge model, as well as the Sage 2.3.0 small molecule force field for drug-like molecules. AshGC is a graph neural network-based method for efficient assignment of charges to both small and large molecules, designed to produce charges of semi- empirical quality at low cost. We describe the model, its architecture, featurization, training process, and performance. AshGC largely generates charges within the range of other accepted AM1-BCC backends such as OpenEye’s oequacpac and AmberTools’ sqm, but we note areas of poor performance such as in smaller molecules and in charged molecules, as well as areas of chemistry underrepresented in the training set. We fur- ther describe the development and performance of Sage 2.3.0, where Lennard-Jones and valence parameters are re-trained with neural network charges for the first time. Our benchmark suite of common force field tasks ranges from optimizing molecular structures in vacuum to protein-ligand binding free energies. Overall, Sage 2.3.0 performs comparably to previous Sage force fields, although we report minor improvements in the prediction of condensed phase properties and a minor decrease in performance in the prediction of non-aqueous solvation free energies. As with other OpenFF force fields, Sage 2.3.0 was validated in protein-ligand benchmarks to be compatible with Amber protein force fields. All data are publicly available, along with scripts, environments, and complete details for reproducing the training and benchmarking of AshGC and Sage 2.3.0 at https://github.com/openforcefield/ashgc-v1.0-fit and github.com/openforcefield/ash-sage-rc2 respectively.