Enhancing protein–ligand binding affinity predictions using neural network potentials

Francesc Sabanés Zariquiey, Raimondas Galvelis, Emilio Gallicchio, John D. Chodera, Thomas E. Markland, Gianni De Fabritiis
Journal of Chemical Information and Modeling 64:1481, 2024.
[DOI] [preprint]

We show that hybrid neural network / molecular mechanics potentials can significantly improve accuracy over molecular mechanics potentials alone in predicting protein-ligand binding affinities.

NNP/MM: Fast molecular dynamics simulations with machine learning potentials and molecular mechanics

Galvelis R, Varela-Rial A, Doerr S, Fino R, Eastman P, Markland TE,  Chodera JD, and de Fabritiis G
Journal of Chemical Information and Modeling 63:5701, 2023 [DOI] [arXiv]

We demonstrate that a new generation of quantum machine learning (QML) potentials based on neural networks---which can achieve quantum chemical accuracy at a fraction of the cost---can be implemented efficiently in the OpenMM molecular dynamics simulation engine as part of hybrid machine learning / molecular mechanics (ML/MM) potentials that promise to deliver superior accuracy for modeling protein-ligand interactions.