QUBEKit: Automating the Derivation of Force Field Parameters from Quantum Mechanics

Modern molecular mechanics force
fields are widely used for modeling
the dynamics and interactions of small organic molecules using libraries
of transferable force field parameters. However, for molecules outside
the training set, the parameters are potentially inaccurate and it
may be preferable to derive molecule-specific parameters. Here we
present an intuitive parameter derivation toolkit, QUBEKit (QUantum
mechanical BEspoke Kit), which enables the automated generation of
system-specific small molecule force field parameters directly from
quantum mechanics. QUBEKit is written in python and combines bond,
angle, torsion, charge, and Lennard-Jones parameter derivation methodologies
alongside a method for deriving the positions and charges of off-center
virtual sites from the partitioned quantum mechanical electron density.
As a proof of concept, we have rederived a complete set of parameters
for 109 small organic molecules and assessed the accuracy by comparing
computed liquid properties with experiments. QUBEKit gives competitive
results when compared to standard transferable force fields, with
mean unsigned errors of 0.024 g/cm3, 0.79 kcal/mol, and
1.17 kcal/mol for the liquid density, heat of vaporization, and free
energy of hydration, respectively. This indicates that the derived
parameters are suitable for molecular modeling applications, including
computer-aided drug design.