基于广义玻恩和可迁移图神经网络的隐式溶剂方法用于分子动力学模拟
Implicit Solvent Approach Based on Generalised Born and Transferable Graph Neural Networks for Molecular Dynamics Simulations
AI总结：
提出基于图神经网络的隐式溶剂方法，可泛化至训练集外肽的显式溶剂效应，兼顾分子动力学模拟的准确性与效率。
摘要：
分子动力学（MD）模拟能够研究大小（生物）分子的运动并估计其构象系综。环境（溶剂）的描述对此具有很大影响。隐式溶剂表示效率高，但在许多情况下不够准确（特别是对于水等极性溶剂）。显式处理溶剂分子更准确，但计算成本更高。最近，机器学习（ML）被提出来弥合这一差距，以隐式方式模拟显式溶剂化效应。然而，当前的方法依赖于对整个构象空间的先验知识，限制了其在实际中的应用。在此，我们引入了一种基于图神经网络（GNN）的隐式溶剂，能够为组成不同于训练集的肽描述显式溶剂效应。
英文摘要：
Molecular dynamics (MD) simulations enable the study of the motion of small and large (bio)molecules and the estimation of their conformational ensembles. The description of the environment (solvent) has thereby a large impact. Implicit solvent representations are efficient but in many cases not accurate enough (especially for polar solvents such as water). More accurate but also computationally more expensive is the explicit treatment of the solvent molecules. Recently, machine learning (ML) has been proposed to bridge the gap and simulate in an implicit manner explicit solvation effects. However, the current approaches rely on prior knowledge of the entire conformational space, limiting their application in practice. Here, we introduce a graph neural network (GNN) based implicit solvent that is capable of describing explicit solvent effects for peptides with different composition than contained in the training set.
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