Enhancing Protein-Ligand Binding Affinity Predictions using Neural Network Potentials

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

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Enhancing Protein-Ligand Binding Affinity Predictions using Neural Network Potentials

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arxiv 2401.16062 v2 pith:H3FZ2JE4 submitted 2024-01-29 physics.chem-ph q-bio.QM

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 This is my paper

classification physics.chem-ph q-bio.QM

keywords bindingaffinitymolecularnetworkneuralpotentialspredictionsprotein-ligand

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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.

Enhancing Protein-Ligand Binding Affinity Predictions using Neural Network Potentials

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