AMARO: All heavy-atom transferable neural network potentials of protein thermodynamics

Mirarchi, Antonio; Peláez, Raúl P.; Simeon, Guillem; De Fabritiis, Gianni

doi:http://dx.doi.org/10.1021/acs.jctc.4c01239

AMARO: All heavy-atom transferable neural network potentials of protein thermodynamics

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Mirarchi A, Peláez RP, Simeon G, De Fabritiis G. AMARO: All heavy-atom transferable neural network potentials of protein thermodynamics. J Chem Theory Comput. 2024 Nov 26;20(22):9871-8. DOI: 10.1021/acs.jctc.4c01239

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.