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.

Open Force Field BespokeFit: Automating Bespoke Torsion Parametrization at Scale

Horton JT, Boothroyd S, Wagner W, Mitchell JA, Gokey T, Dotson DL, Behara PK, Ramaswamy VK, Mackey M, Chodera JD, Anwar J, Mobley DL, and Cole DJ
Journal of Chemical Informatics and Modeling 62:22, 2022 [DOI]

We describe an automated pipeline for generating tailored force field parameters for small molecules using quantum chemical or quantum machine learning potentials.