Enhancing Protein-Ligand Binding Affinity Predictions using Neural Network Potentials

This letter gives results on improving protein-ligand binding affinity predictions based on molecular dynamics simulations using machine learning potentials with a hybrid neural network potential and molecular mechanics methodology (NNP/MM). We compute relative binding free energies (RBFE) with the...

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Bibliographic Details
Published in:ArXiv.org 2024-02
Main Authors: Zariquiey, Francesc Sabanes, Galvelis, Raimondas, Gallicchio, Emilio, Chodera, John D, Markland, Thomas E, de Fabritiis, Gianni
Format: Article
Language:English
Online Access:Get full text
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Summary:This letter gives results on improving protein-ligand binding affinity predictions based on molecular dynamics simulations using machine learning potentials with a hybrid neural network potential and molecular mechanics methodology (NNP/MM). We compute relative binding free energies (RBFE) with the Alchemical Transfer Method (ATM) and validate its performance against established benchmarks and find significant enhancements compared to conventional MM force fields like GAFF2.
ISSN:2331-8422
2331-8422