Machine Learning Enhanced Calculation of Quantum-Classical Binding Free Energies
Abstract
Binding free energies are a key element 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 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 description 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 anti-cancer drug NKP1339 acting on the glucose-regulated protein 78.
Keywords
Cite
@article{arxiv.2503.03955,
title = {Machine Learning Enhanced Calculation of Quantum-Classical Binding Free Energies},
author = {Moritz Bensberg and Marco Eckhoff and F. Emil Thomasen and William Bro-Jørgensen and Matthew S. Teynor and Valentina Sora and Thomas Weymuth and Raphael T. Husistein and Frederik E. Knudsen and Anders Krogh and Kresten Lindorff-Larsen and Markus Reiher and Gemma C. Solomon},
journal= {arXiv preprint arXiv:2503.03955},
year = {2025}
}