AMARO用神经网络实现蛋白质热力学模拟，省去氢原子仍保持稳定高效。
AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics
- 基于张量网络与原子转移架构，忽略氢原子构建粗粒度势能函数
- 无需预设能量项即可训练出稳定蛋白动力学的神经网络势
- 适合大规模蛋白质构象演化模拟，尤其适用于长时序计算
全原子分子模拟能提供大分子过程的精细洞察，但其高昂的计算成本限制了对复杂生物过程的探索。我们提出Advanced Machine-learning Atomic Representation Omni-force-field（AMARO），一种新型神经网络势（NNP），结合了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。AMARO证明了在不依赖先验能量项的情况下，训练更粗粒度的神经网络势是可行的，可实现稳定的蛋白质动力学模拟，并具备良好的可扩展性与泛化能力。
原文摘要 · Abstract (English)
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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