Advances in Machine Learning, Limitations, and Performance Insight of Commonly used Computation Chemistry Methods for Solvation Free Energy Estimation

<jats:p> The accurate calculation of Gibbs free energy is a cornerstone of modern computational chemistry, essential for predicting the spontaneity of chemical reactions and the stability of molecular systems. Solvation free energy change is a fundamental thermodynamic quantity that describes the free energy change associated with transferring a solute from the gas phase into a solvent. It is particularly useful for estimating reaction rates and pathways of organic reactions, most of which occur in the liquid phase, and has important applications in organic synthesis, biochemistry, and drug discovery. <jats:sup>1</jats:sup> Central and persistent challenges are the choice of implicit and explicit models, delicate balance between achieving high accuracy in quantum mechanical descriptions of electronic structure and performing the extensive configurational sampling required to capture entropic effects, continual evolving of foundational statistical mechanics methods such as Free Energy Perturbation (FEP) and Thermodynamic Integration (TI) to more sophisticated multiscale and hybrid approaches for higher computational efficiency. <jats:sup>2,3,4</jats:sup> This report provides an overview of the theoretical frameworks, implementations, and predictive accuracy of commonly used methods for calculating solvation free energy , assessing classical alchemical methods, quantum chemistry based methods, and hybrid QM/MM approaches, while also addressing practical considerations. Finally, we examine the potential transformative role of emerging methods such as machine learning, which are poised to accelerate these calculations, making rigorous, large-scale free energy simulations feasible and better balancing the cost-accuracy trade-off that has long constrained the field. <jats:sup>5,6</jats:sup> </jats:p>