分子潜空间模拟器
Molecular Latent Space Simulators
摘要：
小的积分时间步长限制了分子动力学（MD）模拟到毫秒时间尺度。马尔可夫状态模型（MSM）和无方程方法通过执行状态空间的配置或动态粗粒度化来从MD模拟数据中学习低维动力学模型。所学习的动力学模型使得能够在比MD可访问的时间尺度长得多的时间尺度上有效地生成动力学轨迹，但是构型空间的离散化和/或缺乏重建分子构型的手段排除了连续的全原子分子轨迹的生成。我们提出了潜在空间模拟器（LSS），通过训练三个深度学习网络来学习连续全原子模拟轨迹的动力学模型，以（i）学习分子系统的缓慢集体变量，（ii）在这个缓慢的潜在空间内传播系统动力学，以及（iii）生成性地重建分子构型。我们证明了该方法在Trp笼miniprotein的应用程序，以产生新的超长合成折叠轨迹，准确地再现全原子分子结构，热力学和动力学在六个数量级的成本低于MD。轨迹生成的成本显著降低，使得能够大大改进采样，并大大降低估计的热力学平均值和动力学速率的统计不确定性。
英文摘要：
Small integration time steps limit molecular dynamics (MD) simulations to millisecond time scales. Markov state models (MSMs) and equation-free approaches learn low-dimensional kinetic models from MD simulation data by performing configurational or dynamical coarse-graining of the state space. The learned kinetic models enable the efficient generation of dynamical trajectories over vastly longer time scales than are accessible by MD, but the discretization of configurational space and/or absence of a means to reconstruct molecular configurations precludes the generation of continuous all-atom molecular trajectories. We propose latent space simulators (LSS) to learn kinetic models for continuous all-atom simulation trajectories by training three deep learning networks to (i) learn the slow collective variables of the molecular system, (ii) propagate the system dynamics within this slow latent space, and (iii) generatively reconstruct molecular configurations. We demonstrate the approach in an application to Trp-cage miniprotein to produce novel ultra-long synthetic folding trajectories that accurately reproduce all-atom molecular structure, thermodynamics, and kinetics at six orders of magnitude lower cost than MD. The dramatically lower cost of trajectory generation enables greatly improved sampling and greatly reduced statistical uncertainties in estimated thermodynamic averages and kinetic rates.
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