用于无集体变量采样分子跃迁路径的随机最优控制
Stochastic Optimal Control for Collective Variable Free Sampling of Molecular Transition Paths
AI总结：
本文将分子跃迁路径采样关联到Schrödinger桥与随机最优控制，提出不依赖集体变量的PIPS机器学习方法，并在丙氨酸二肽、聚脯氨酸和Chignolin上生成低能跃迁路径。
摘要：
我们考虑在分子系统的两个给定亚稳态之间采样跃迁路径的问题，例如折叠与未折叠蛋白质之间，或化学反应的产物与反应物之间。由于分隔这些状态的高能垒的存在，使用标准分子动力学（MD）模拟不太可能采样到这些跃迁路径。通过偏置势增强MD以提高跃迁概率的传统方法依赖于基于集体变量（CVs）的降维步骤。遗憾的是，选择合适的CVs需要化学直觉，因此传统方法并不总是适用于更大的系统。此外，当使用不正确的CVs时，偏置势可能不是最小的，并会沿与跃迁无关的维度对系统施加偏置。通过展示分子跃迁路径采样问题、Schrödinger桥问题以及采用神经网络策略的随机最优控制之间的形式化关系，我们提出了一种用于采样上述跃迁的机器学习方法。与以往的非机器学习方法不同，我们名为PIPS的方法不依赖于CVs。我们表明，该方法能够成功为丙氨酸二肽以及更大的聚脯氨酸和Chignolin蛋白质生成低能跃迁。
英文摘要：
We consider the problem of sampling transition paths between two given metastable states of a molecular system, e.g. a folded and unfolded protein or products and reactants of a chemical reaction. Due to the existence of high energy barriers separating the states, these transition paths are unlikely to be sampled with standard Molecular Dynamics (MD) simulation. Traditional methods to augment MD with a bias potential to increase the probability of the transition rely on a dimensionality reduction step based on Collective Variables (CVs). Unfortunately, selecting appropriate CVs requires chemical intuition and traditional methods are therefore not always applicable to larger systems. Additionally, when incorrect CVs are used, the bias potential might not be minimal and bias the system along dimensions irrelevant to the transition. Showing a formal relation between the problem of sampling molecular transition paths, the Schrödinger bridge problem and stochastic optimal control with neural network policies, we propose a machine learning method for sampling said transitions. Unlike previous non-machine learning approaches our method, named PIPS, does not depend on CVs. We show that our method successful generates low energy transitions for Alanine Dipeptide as well as the larger Polyproline and Chignolin proteins.
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