AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics
<|reference_start|>AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics: 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.<|reference_end|>
@article{mirarchi2024amaro:,
    title={AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein
  Thermodynamics},
    author={Antonio Mirarchi, Raul P. Pelaez, Guillem Simeon, Gianni De Fabritiis},
    journal={arXiv preprint arXiv:2409.17852},
    year={2024},
    doi={10.1021/acs.jctc.4c01239},
    archivePrefix={arXiv},
    eprint={2409.17852},
    primaryClass={q-bio.BM cs.LG physics.bio-ph physics.comp-ph}
}