Enhancing protein-ligand binding affinity predictions using neural network potentials

Sabanés Zariquiey, Francesc; Galvelis, Raimondas; Gallicchio, Emilio; Chodera, John D.; Markland, Thomas E.; De Fabritiis, Gianni

doi:http://dx.doi.org/10.1021/acs.jcim.3c02031

Enhancing protein-ligand binding affinity predictions using neural network potentials

Citation

Sabanés Zariquiey F, Galvelis R, Gallicchio E, Chodera JD, Markland TE, De Fabritiis G. Enhancing protein-ligand binding affinity predictions using neural network potentials. J Chem Inf Model. 2024 Mar 11;64(5):1481-5. DOI: 10.1021/acs.jcim.3c02031

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 with the Alchemical Transfer Method and validate its performance against established benchmarks and find significant enhancements compared with conventional MM force fields like GAFF2.