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.
翻译:机械学的粗粒粒子模型有可能模拟超出原子分子动态可能范围的大型分子复合体。然而,培训精确的CG模型仍是一项挑战。一种广泛使用的方法,用于从全原子分子动态到CG代表处学习CG力阵列地图,并平均与CG力场相匹配。我们显示,在如何将所有原子力量映射到CG代表处方面,具有灵活性,最常用的绘图方法在统计上效率低,在全原子模拟存在限制的情况下甚至可能不正确。我们为武力映射定义了一个优化说明,并表明在使用优化的武力地图时,可以从相同的模拟数据中学习大大改进CG力场。该方法在微型Protein Chignolin和Tryptophan Cage上得到了演示,并作为开放源代码发布。