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

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-centered symmetry functions for QM/MM data as an ML descriptor. The ML potential approach takes electrostatic embedding and long-range electrostatics into account. We demonstrate the applicability of the workflow on the well-studied protein–ligand complex of myeloid cell leukemia 1 and the inhibitor 19G and on the anticancer drug NKP1339 acting on the glucose-regulated protein 78.