  Fast and accurate molecular force field (FF) parameterization is still an
unsolved problem. Accurate FFs are not generally available for all molecules,
like novel druglike molecules. While methods based on quantum mechanics (QM)
exist to parameterize them with better accuracy, they are computationally
expensive and slow, which limits applicability to a small number of molecules.
Here, we present an automated FF parameterization method which can utilize
either DFT calculations or approximate QM energies produced by different neural
network potentials (NNPs), to obtain improved parameters for molecules. We
demonstrate that for the case of torchani-ANI-1x NNP, we can parameterize small
molecules in a fraction of time compared with an equivalent parameterization
using DFT QM calculations while producing more accurate parameters than FF
(GAFF2). We expect our method to be of critical importance in computational
structure-based drug discovery. The current version is available at
PlayMolecule (www.playmolecule.org) and implemented in HTMD, allowing to
parameterize molecules with different QM and NNP options.
