Machine Learning-Enhanced Calculation of Quantum-Classical Binding Free Energies.

ABSTRACT: Binding free energies are key elements in understanding and predicting the strength of protein-drug interactions. While classical free energy simulations yield good results for many purely organic ligands, drugs, including transition metal atoms, often require quantum chemical methods for an accurate description. We propose a general and automated workflow that samples the potential energy surface with hybrid quantum mechanics/molecular mechanics (QM/MM) calculations and trains a machine learning (ML) potential on the QM/MM energies and forces to enable efficient alchemical free energy simulations. To represent systems including many different chemical elements efficiently and to account for the different descriptions of QM and MM atoms, we propose an extension of element-embracing atom-ce

SUBMITTER: Bensberg M 

PROVIDER: S-EPMC12392446 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature