Enhancing Protein-Ligand Binding Affinity Predictions using Neural Network Potentials
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arXiv
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| Format: | Preprint |
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2024
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| author | Zariquiey, Francesc Sabanes Galvelis, Raimondas Gallicchio, Emilio Chodera, John D. Markland, Thomas E. de Fabritiis, Gianni |
| author_facet | Zariquiey, Francesc Sabanes Galvelis, Raimondas Gallicchio, Emilio Chodera, John D. Markland, Thomas E. de Fabritiis, Gianni |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_16062 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Protein-Ligand Binding Affinity Predictions using Neural Network Potentials Zariquiey, Francesc Sabanes Galvelis, Raimondas Gallicchio, Emilio Chodera, John D. Markland, Thomas E. de Fabritiis, Gianni Chemical Physics Quantitative Methods 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. |
| title | Enhancing Protein-Ligand Binding Affinity Predictions using Neural Network Potentials |
| topic | Chemical Physics Quantitative Methods |
| url | https://arxiv.org/abs/2401.16062 |