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arXiv · 2409.17852

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

Antonio Mirarchi·Raul P. Pelaez·Guillem Simeon·Gianni De Fabritiis
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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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Antonio Mirarchi, Raul P. Pelaez, Guillem Simeon, Gianni De Fabritiis. 2024-09-26. AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics. https://doi.org/10.1021/acs.jctc.4c01239

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