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

ABSTRACT: 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 capa

SUBMITTER: Mutlu E 

PROVIDER: S-EPMC12703735 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature