Generation of Quantum Configurational Ensembles Using Approximate Potentials

Conformational
analysis is of paramount importance in drug design:
it is crucial to determine pharmacological properties, understand
molecular recognition processes, and characterize the conformations of ligands when unbound.
Molecular Mechanics (MM) simulation methods, such as Monte Carlo (MC)
and molecular dynamics (MD), are usually employed to generate ensembles
of structures due to their ability to extensively sample the conformational
space of molecules. The accuracy of these MM-based schemes strongly
depends on the functional form of the force field (FF) and its parametrization,
components that often hinder their performance. High-level methods,
such as ab initio MD, provide reliable structural
information but are still too computationally expensive to allow for
extensive sampling. Therefore, to overcome these limitations, we present
a multilevel MC method that is capable of generating quantum configurational
ensembles while keeping the computational cost at a minimum. We show
that FF reparametrization is an efficient route to generate FFs that
reproduce QM results more closely, which, in turn, can be used as
low-cost models to achieve the gold standard QM accuracy. We demonstrate
that the MC acceptance rate is strongly correlated with various phase
space overlap measurements and that it constitutes a robust metric
to evaluate the similarity between the MM and QM levels of theory.
As a more advanced application, we present a self-parametrizing version
of the algorithm, which combines sampling and FF parametrization in
one scheme, and apply the methodology to generate the QM/MM distribution
of a ligand in aqueous solution.