Physics-Based Machine Learning to Predict Hydration Free Energies for Small Molecules with a Minimal Number of Descriptors: Interpretable and Accurate

Hydration free energy (HFE) of molecules is a fundamental property having importance throughout chemistry and biology. Calculation of the HFE can be challenging and expensive with classical molecular dynamics simulation-based approaches. Machine learning (ML) models are increasingly being used to predict HFE. Although the accuracy of ML models for data sets for small molecules is impressive, these models suffer from lack of interpretability. In this work, we have developed a physics-based ML model with only six descriptors, which is both accurate and fully interpretable, and applied it to a database for small molecule HFE, <i>FreeSolv</i>. We evaluated the electrostatic energy by an approximate closed form of the Generalized Born (GB) model and polar surface area. In addition, we have log <i>P</i> and hydrogen bond acceptor and donors as descriptors along with the number of rotatable bonds. We have used different ML models, such as random forest and extreme gradient boosting. The best result from these models has a mean absolute error of only 0.74 kcal/mol. The main power of this model is that the descriptors have clear physical meaning, and it was found that the descriptor describing the electrostatics and the polar surface area, followed by the hydrogen bond donors and acceptors, are the most important factors for the calculation of hydration free energy.