粗粒化分子动力学的统计最优力聚集
Statistically Optimal Force Aggregation for Coarse-Graining Molecular Dynamics
摘要：
机器学习的粗粒度（CG）模型具有模拟大分子复合物的潜力，超出了原子分子动力学的可能性。然而，训练准确的CG模型仍然是一个挑战。一种广泛使用的学习CG力场的方法将来自全原子分子动力学的力映射到CG表示，并将它们与平均CG力场相匹配。我们表明，有灵活性，如何映射到CG表示的所有原子的力量，最常用的映射方法是统计效率低下，甚至可能不正确的存在约束的所有原子模拟。我们定义了一个优化语句的力映射，并表明，大大改善CG力场可以从相同的模拟数据时，使用优化的力映射。该方法在微蛋白Chignolin和Tryptophan Cage上进行了演示，并作为开源代码发布。
英文摘要：
Machine-learned coarse-grained (CG) models have the potential for simulating large molecular complexes beyond what is possible with atomistic molecular dynamics. However, training accurate CG models remains a challenge. A widely used methodology for learning CG force-fields maps forces from all-atom molecular dynamics to the CG representation and matches them with a CG force-field on average. We show that there is flexibility in how to map all-atom forces to the CG representation, and that the most commonly used mapping methods are statistically inefficient and potentially even incorrect in the presence of constraints in the all-atom simulation. We define an optimization statement for force mappings and demonstrate that substantially improved CG force-fields can be learned from the same simulation data when using optimized force maps. The method is demonstrated on the miniproteins Chignolin and Tryptophan Cage and published as open-source code.
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