用于增强采样的重加权自编码变分贝叶斯（RAVE）
Reweighted Autoencoded Variational Bayes for Enhanced Sampling (RAVE)
摘要：
在这里，我们提出了用于增强采样的重加权自编码变分贝叶斯（RAVE）方法，这是一种新的迭代方案，它使用变分自编码器的深度学习框架来增强分子模拟中的采样。RAVE涉及分子模拟和深度学习之间的迭代，以便沿着低维潜在空间产生越来越准确的概率分布，从而捕获分子模拟轨迹的关键特征。使用这种潜在的空间分布和从分子模拟中采样的各种试验反应坐标的分布之间的Kullback-Leibler发散，RAVE确定最佳的，然而物理上可解释的反应坐标和最佳概率分布。然后，两者都直接用作新的有偏模拟的偏置协议，该偏置协议再次被馈送到深度学习模块中，其中适当的权重考虑了偏置，该过程继续进行，直到期望的热力学可观测量的估计收敛。与最近使用深度学习进行增强采样的方法不同，RAVE的突出之处在于：（a）它自然地产生物理上可解释的反应坐标，（b）独立于现有的增强采样协议，以增强通过深度学习识别的潜在空间的波动，以及（c）它提供了轻松过滤掉深度学习过程中学习到的虚假解决方案的能力。RAVE的有用性和可靠性证明，通过将其应用到模型的潜力越来越复杂，包括计算的结合自由能配置文件的疏水配体-底物系统在明确的水与解离时间超过3分钟，在计算机的时间至少20倍，比伞采样或metadaptics所需的。
英文摘要：
Here we propose the Reweighted Autoencoded Variational Bayes for Enhanced Sampling (RAVE) method, a new iterative scheme that uses the deep learning framework of variational autoencoders to enhance sampling in molecular simulations. RAVE involves iterations between molecular simulations and deep learning in order to produce an increasingly accurate probability distribution along a low-dimensional latent space that captures the key features of the molecular simulation trajectory. Using the Kullback-Leibler divergence between this latent space distribution and the distribution of various trial reaction coordinates sampled from the molecular simulation, RAVE determines an optimum, yet nonetheless physically interpretable, reaction coordinate and optimum probability distribution. Both then directly serve as the biasing protocol for a new biased simulation, which is once again fed into the deep learning module with appropriate weights accounting for the bias, the procedure continuing until estimates of desirable thermodynamic observables are converged. Unlike recent methods using deep learning for enhanced sampling purposes, RAVE stands out in that (a) it naturally produces a physically interpretable reaction coordinate, (b) is independent of existing enhanced sampling protocols to enhance the fluctuations along the latent space identified via deep learning, and (c) it provides the ability to easily filter out spurious solutions learned by the deep learning procedure. The usefulness and reliability of RAVE is demonstrated by applying it to model potentials of increasing complexity, including computation of the binding free energy profile for a hydrophobic ligand-substrate system in explicit water with dissociation time of more than three minutes, in computer time at least twenty times less than that needed for umbrella sampling or metadynamics.
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