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.

中文翻译：

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AMARO：蛋白质热力学的所有重原子可传递神经网络势


全原子分子模拟提供了对大分子现象的详细见解，但其大量的计算成本阻碍了对复杂生物过程的探索。我们介绍了高级机器学习原子表示全力场 （AMARO），这是一种新的神经网络势 （NNP），它结合了 O（3） 等变消息传递神经网络架构 TensorNet 和排除氢原子的粗粒度映射。AMARO 展示了在没有先验能量项的情况下训练较粗的 NNP 以运行具有可扩展性和泛化能力的稳定蛋白质动力学的可行性。