AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics

Antonio Mirarchi, Raul P. Pelaez, Guillem Simeon, Gianni De Fabritiis
9/26/2024
q-bio.BMcs.LGphysics.bio-ph

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Abstract

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.

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Cite this paper

@article{mirarchi2024amaro,
  title  = {AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics},
  author = {Antonio Mirarchi and Raul P. Pelaez and Guillem Simeon and Gianni De Fabritiis},
  year   = {2024},
  eprint = {2409.17852},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2409.17852v3}
}

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