Prediction of Charged Small Molecule Conformations in Solution Using a Balanced ML/MM Potential

Reliably
evaluating the most stable conformations of charged small
organic molecules in solution poses a major challenge to computational
chemistry, due to limitations in the accuracy or efficiency of conventional
methodologies. In this paper, we present a hybrid machine learning/molecular
mechanics (ML/MM) potential, with dynamic, conformationally dependent
charges on the atoms in the ML region, that can be used to address
this important challenge in drug design. By way of a case study, metadynamics-enhanced
molecular dynamics simulations were used to compare the performance
of several intermolecular potentials in evaluating the solution phase
conformational free energy differences of pharmaceutically relevant
ligands based on the protonated 2-phenylethylamine scaffold. A straightforward
approach to an ML/MM potential, in which the solute’s intramolecular
interactions are substituted by an ML model and making no other modifications
to the force field, yields results inferior to a conventional fixed-charge
MM potential, due to an imbalance created in the intra- and intermolecular
interactions. To remedy this shortcoming, we present a new ML/MM potential
that combines an accurate, trained-for-purpose ML model (PairFEQ-Net)
of the charged ligand, with the polarizable SWM3 MM model of water.
Crucially, by employing an empirical parameter to scale gas phase
charges, the ML model predicts dynamic, solvent-polarized charges
that embed the ligand in the solvent in a more balanced way. This
ML/MM potential results in mean absolute errors in the prediction
of conformational free energies of just 0.5 kcal mol–1, hence furnishing a possible route to the chemically accurate prediction
of the shapes of charged organic molecules in aqueous solution.