Bayesian Model Averaging for Ensemble-Based Estimates of Solvation Free Energies

TL;DR

This paper introduces a Bayesian Model Averaging ensemble method to improve the accuracy of solvation free energy predictions by combining multiple models, significantly reducing errors compared to individual methods.

Contribution

The paper presents an iterative BMA ensemble approach that outperforms individual models and other ensemble methods in predicting solvation free energies.

Findings

Ensemble reduces prediction error by up to 91%.

Iterative process outperforms other ensemble approaches.

Final estimate achieves 1.2 kcal/mol accuracy.

Abstract

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.