Accurate Hydration Free Energy Calculations for Diverse Organic Molecules With a Machine Learning Force Field

Free energy perturbation (FEP) calculations using classical force fields remain the dominant approach for large-scale, computational drug discovery efforts but the accuracy is fundamentally limited by simplified forms that cannot quantitatively reproduce ab initio methods without significant fine tuning. Machine Learning force fields (MLFFs) offer a promising avenue to retain quantum mechanical accuracy with significantly reduced computational cost compared to ab initio molecular dynamics (AIMD) simulations. Thus far, direct applications of ML force fields to FEP calculations lack systematic protocols and extensive benchmarking. In this work, we take a step in this direction by presenting a general and robust workflow for solvation (hydration) free energy (HFE) calculations which is independent of the details of the particular MLFF architecture used. Combining a broadly trained ML force field, Organic_MPNICE, with sufficient statistical and conformational sampling empowered by the solute-tempering technique, affords sub-kcal/mol average errors in HFE predictions relative to experimental estimates. This approach outperforms state-of-the-art classical force fields and DFT-based implicit solvation models on a diverse set of 59 organic molecules and provides a route to ab initio-quality HFE predictions, advancing the use of ML force fields in thermodynamic property prediction.