  This paper applies the Bayesian Model Averaging (BMA) statistical ensemble
technique to estimate small molecule solvation free energies. There is a wide
range of methods available for predicting solvation free energies, ranging from
empirical statistical models to ab initio quantum mechanical approaches. Each
of these methods is based on a set of conceptual assumptions that can affect
predictive accuracy and transferability. Using an iterative statistical
process, we have selected and combined solvation energy estimates using an
ensemble of 17 diverse methods from the fourth Statistical Assessment of
Modeling of Proteins and Ligands (SAMPL) blind prediction study to form a
single, aggregated solvation energy estimate. The ensemble design process
evaluates the statistical information in each individual method as well as the
performance of the aggregate estimate obtained from the ensemble as a whole.
Methods that possess minimal or redundant information are pruned from the
ensemble and the evaluation process repeats until aggregate predictive
performance can no longer be improved. We show that this process results in a
final aggregate estimate that outperforms all individual methods by reducing
estimate errors by as much as 91% to 1.2 kcal/mol accuracy. We also compare our
iterative refinement approach to other statistical ensemble approaches and
demonstrate that this iterative process reduces estimate errors by as much as
61%. This work provides a new approach for accurate solvation free energy
prediction and lays the foundation for future work on aggregate models that can
balance computational cost with prediction accuracy.
