Data-Efficient Equivariant NNPs Enable DFT-Accurate Simulations and Implicit Solvation Free Energies

The use of machine learning (ML) potentials has emerged as a powerful approach in computational chemistry, particularly in computer-aided drug design studies. Neural network potentials (NNPs) provide a more physics-informed estimation of binding and solvation events, bridging the accuracy gap between classical force fields and quantum mechanical methods. Current universal neural network potentials (NNPs) have not yet achieved consistent chemical accuracy required for reliable molecular dynamics simulations. Accurate yet data efficient representation of potential energy surfaces and prediction of solvation free energies are essential for large-scale molecular simulations and drug-design workflows. Here, we utilize data efficient E(3)-equivariant graph neural network potentials that are capable of estimating the solvation free energies (SFEs) of small compounds with density functional theory (DFT)-level accuracy and significantly reduced computational cost. Leveraging the data efficiency of equivariant architectures, our models achieve chemical accuracy with a relatively small training data set. We demonstrate that the method is data-efficient in constructing ML potentials. Our focus is on hydration free energy changes of small compounds from the FreeSolv database. We develop and test two distinct NNPsone for the gas phase and one for an implicit water modelthat can be applied in molecular simulations and SFE calculations using implicit solvation. The Solvation Model based on Density (SMD)-based implicit NNP model achieves an accuracy of 89% while offering a substantial computational speed-up compared to its DFT counterpart, which attains 90% accuracy.