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.

翻译：全原子分子模拟为宏观分子现象提供了细致的洞察，但其高昂的计算成本阻碍了对复杂生物过程的探索。我们提出了先进机器学习原子表示全力场（AMARO），这是一种新型神经网络势能（NNP），它将O(3)等变消息传递神经网络架构TensorNet与排除氢原子的粗粒化映射相结合。AMARO证明了在没有先验能量项的情况下，训练更粗粒度的NNP以运行稳定的蛋白质动力学并具备可扩展性和泛化能力的可行性。