AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866913575838154752 |
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| author | Mirarchi, Antonio Pelaez, Raul P. Simeon, Guillem De Fabritiis, Gianni |
| author_facet | Mirarchi, Antonio Pelaez, Raul P. Simeon, Guillem De Fabritiis, Gianni |
| contents | All-atom molecular simulations offer detailed insights into macromolecular phenomena, but their substantial computational cost hinders the exploration of complex biological processes. We introduce Advanced Machine-learning Atomic Representation Omni-force-field (AMARO), a new neural network potential (NNP) that combines an O(3)-equivariant message-passing neural network architecture, TensorNet, with a coarse-graining map that excludes hydrogen atoms. AMARO demonstrates the feasibility of training coarser NNP, without prior energy terms, to run stable protein dynamics with scalability and generalization capabilities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_17852 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics Mirarchi, Antonio Pelaez, Raul P. Simeon, Guillem De Fabritiis, Gianni Biomolecules Machine Learning Biological Physics Computational Physics All-atom molecular simulations offer detailed insights into macromolecular phenomena, but their substantial computational cost hinders the exploration of complex biological processes. We introduce Advanced Machine-learning Atomic Representation Omni-force-field (AMARO), a new neural network potential (NNP) that combines an O(3)-equivariant message-passing neural network architecture, TensorNet, with a coarse-graining map that excludes hydrogen atoms. AMARO demonstrates the feasibility of training coarser NNP, without prior energy terms, to run stable protein dynamics with scalability and generalization capabilities. |
| title | AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics |
| topic | Biomolecules Machine Learning Biological Physics Computational Physics |
| url | https://arxiv.org/abs/2409.17852 |