Enhancing Protein-Ligand Binding Affinity Predictions using Neural Network Potentials

Francesc Sabanes Zariquiey; Raimondas Galvelis; Emilio Gallicchio; John D. Chodera; Thomas E. Markland; Gianni de Fabritiis

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arXiv · 2401.16062

Enhancing Protein-Ligand Binding Affinity Predictions using Neural Network Potentials

Francesc Sabanes Zariquiey·Raimondas Galvelis·Emilio Gallicchio·John D. Chodera·Thomas E. Markland·Gianni de Fabritiis

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Abstract

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.

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Francesc Sabanes Zariquiey, Raimondas Galvelis, Emilio Gallicchio, John D. Chodera, Thomas E. Markland, Gianni de Fabritiis. 2024-01-29. Enhancing Protein-Ligand Binding Affinity Predictions using Neural Network Potentials. https://arxiv.org/abs/2401.16062

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