mlsanityData to 7 Oct 2026

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

arXiv 2409.17852 ↗First posted 26 Sep 2024q-bio.BM cs.LG physics.bio-phcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 15 of its 60 references that are in the map. First posted on arXiv 26 Sep 2024.

Builds on

15 of 60 references.

References · 15

  1. 2021E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentialscited by 701
  2. 2022MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fieldscited by 467
  3. 2022Learning Local Equivariant Representations for Large-Scale Atomistic Dynamicscited by 254
  4. 2019Machine learning for molecular simulationcited by 109
  5. 2018Machine Learning of coarse-grained Molecular Dynamics Force Fieldscited by 68
  6. 2023A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systemscited by 65
  7. 2020Coarse Graining Molecular Dynamics with Graph Neural Networkscited by 56
  8. 2020TorchMD: A deep learning framework for molecular simulationscited by 55
  9. 2023TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentialscited by 53
  10. 2022Machine Learning Coarse-Grained Potentials of Protein Thermodynamicscited by 37
  11. 2023Navigating protein landscapes with a machine-learned transferable coarse-grained modelcited by 22
  12. 2021Machine Learning Implicit Solvation for Molecular Dynamicscited by 16
3 more
  1. 2024TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulationscited by 15
  2. 2023Statistically Optimal Force Aggregation for Coarse-Graining Molecular Dynamicscited by 9
  3. 2023Top-down machine learning of coarse-grained protein force-fieldscited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentialscited by 3
  2. 2025Operator Forces For Coarse-Grained Molecular Dynamicscited by 0
  3. 2026Coarse-Grained Boltzmann Generatorscited by 0