ParaMol: A Package for Automatic Parameterization of Molecular Mechanics Force Fields

The
ensemble of structures generated by molecular mechanics (MM)
simulations is determined by the functional form of the force field
employed and its parameterization. For a given functional form, the
quality of the parameterization is crucial and will determine how
accurately we can compute observable properties from simulations.
While accurate force field parameterizations are available for biomolecules,
such as proteins or DNA, the parameterization of new molecules, such
as drug candidates, is particularly challenging as these may involve
functional groups and interactions for which accurate parameters may
not be available. Here, in an effort to address this problem, we present
ParaMol, a Python package that has a special focus on the parameterization
of bonded and nonbonded terms of druglike molecules by fitting to ab initio data. We demonstrate the software by deriving
bonded terms’ parameters of three widely known drug molecules, viz. aspirin, caffeine, and a norfloxacin analogue, for
which we show that, within the constraints of the functional form,
the methodologies implemented in ParaMol are able to derive near-ideal
parameters. Additionally, we illustrate the best practices to follow
when employing specific parameterization routes. We also determine
the sensitivity of different fitting data sets, such as relaxed dihedral
scans and configurational ensembles, to the parameterization procedure,
and discuss the features of the various weighting methods available
to weight configurations. Owing to ParaMol’s capabilities,
we propose that this software can be introduced as a routine step
in the protocol normally employed to parameterize druglike molecules
for MM simulations.