Enhancing Accuracy and Feature Insights in Hydration Free Energy Predictions for Small Molecules with Machine Learning

The accurate prediction of solvation free energy is of significant importance as it governs the behavior of solutes in solution. In this work, we apply a variety of machine learning techniques to predict and analyze the alchemical free energy of small molecules. Our methodology incorporates an ensemble of machine learning models with feature processing using the K-nearest neighbors algorithm. Two training strategies are explored: one based on experimental data, and the other based on the offset between molecular dynamics (MD) simulations and experimental measurements. The latter approach yields a substantial improvement in predictive accuracy, achieving a mean unsigned error (MUE) of 0.64 kcal/mol. Feature analysis identifies molecular geometry and topology as the most critical factors in predicting alchemical free energy, supporting the established theory that surface tension is a key determinant. Furthermore, the feature analysis of offset results highlights the relevance of charge distribution within the system, which correlates with the inaccuracies in force fields employed in MD simulations and may provide guidance for improving force field designs. These results suggest that machine learning approaches can effectively capture the complex features governing solvation free energy, offering novel pathways for enhancing predictive accuracy.