Molecular dynamics (MD) simulation is a widely used technique to simulate molecular systems, most commonly at the all-atom resolution where the equations of motion are integrated with timesteps on the order of femtoseconds ($1\textrm{fs}=10^{-15}\textrm{s}$). MD is often used to compute equilibrium properties, which requires sampling from an equilibrium distribution such as the Boltzmann distribution. However, many important processes, such as binding and folding, occur over timescales of milliseconds or beyond, and cannot be efficiently sampled with conventional MD. Furthermore, new MD simulations need to be performed from scratch for each molecular system studied. We present Timewarp, an enhanced sampling method which uses a normalising flow as a proposal distribution in a Markov chain Monte Carlo method targeting the Boltzmann distribution. The flow is trained offline on MD trajectories and learns to make large steps in time, simulating the molecular dynamics of $10^{5} - 10^{6}\:\textrm{fs}$. Crucially, Timewarp is transferable between molecular systems: once trained, we show that it generalises to unseen small peptides (2-4 amino acids), exploring their metastable states and providing wall-clock acceleration when sampling compared to standard MD. Our method constitutes an important step towards developing general, transferable algorithms for accelerating MD.

翻译：摘要：分子动力学（MD）模拟是一种广泛应用于模拟分子系统的技术，通常在全原子分辨率下进行，其中运动方程以飞秒量级（$1\textrm{fs}=10^{-15}\textrm{s}$）的时间步长积分。MD常被用于计算平衡性质，这需要从玻尔兹曼分布等平衡分布中采样。然而，许多重要过程（如结合和折叠）在毫秒或更长时间尺度上发生，且无法通过传统MD高效采样。此外，对于每个研究的分子系统，都需要从头开始执行新的MD模拟。我们提出Timewarp，一种增强采样方法，该方法利用归一化流作为针对玻尔兹曼分布的马尔可夫链蒙特卡洛方法中的提议分布。该流通过MD轨迹离线训练，学习在时间上迈出大步，模拟$10^{5} - 10^{6}\:\textrm{fs}$的分子动力学。关键的是，Timewarp在分子系统之间具有可迁移性：一旦训练完成，我们证明它能泛化到未见的小肽（2-4个氨基酸），探索其亚稳态，并在采样时相比标准MD提供实时加速。我们的方法为开发通用的、可迁移的加速MD算法迈出了重要一步。