Molecular dynamics (MD) simulations have emerged to become the back- bone of today's computational biophysics. Simulation tools such as, NAMD, AMBER and GROMACS have accumulated more than 100,000 users. Despite this remarkable success, now also bolstered by compatibility with graphics processor units (GPUs) and exascale computers, even the most scalable simulations cannot access biologically relevant timescales - the number of numerical integration steps necessary for solving differential equations in a million-to-billion-dimensional space is computationally in- tractable. Recent advancements in Deep Learning has made it such that patterns can be found in high dimensional data. In addition, Deep Learning have also been used for simulating physical dynamics. Here, we utilize LSTMs in order to predict future molecular dynamics from current and previous timesteps, and examine how this physics-guided learning can benefit researchers in computational biophysics. In particular, we test fully connected Feed-forward Neural Networks, Recurrent Neural Networks with LSTM / GRU memory cells with TensorFlow and PyTorch frame- works trained on data from NAMD simulations to predict conformational transitions on two different biological systems. We find that non-equilibrium MD is easier to train and performance improves under the assumption that each atom is independent of all other atoms in the system. Our study represents a case study for high-dimensional data that switches stochastically between fast and slow regimes. Applications of re- solving these sets will allow real-world applications in the interpretation of data from Atomic Force Microscopy experiments.

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仔细阅读蛋白质：深度学习以预测分子动力学

分子动力学（MD）模拟已经成为当今计算生物物理学的基础。诸如NAMD，AMBER和GROMACS之类的仿真工具已经积累了超过100,000个用户。尽管取得了令人瞩目的成就，但现在也获得了与图形处理器单元（GPU）和百亿亿次计算机的兼容性的支持，即使是最具扩展性的仿真也无法访问生物学相关的时标-解决百万分之一至十亿微分方程所需的数值积分步骤数三维空间在计算上难以处理。深度学习的最新进展使得它可以在高维数据中找到模式。此外，深度学习也已用于模拟物理动力学。这里，我们利用LSTM来预测当前和先前时间步长中的未来分子动力学，并研究这种以物理学为指导的学习如何使计算生物物理学的研究人员受益。特别是，我们测试了全连接的前馈神经网络，带有带有TensorFlow和PyTorch框架的LSTM / GRU存储器单元的递归神经网络，并根据来自NAMD模拟的数据进行了训练，以预测两种不同生物系统上的构象转变。我们发现，在每个原子独立于系统中所有其他原子的假设下，非平衡MD易于训练，并且性能得到改善。我们的研究代表了一个高维数据的案例研究，该数据在快速和慢速状态之间随机切换。