论文标题
分子动力学优化的强化学习：随机蓬松蛋白最大原理方法
Reinforcement Learning for Molecular Dynamics Optimization: A Stochastic Pontryagin Maximum Principle Approach
论文作者
论文摘要
在本文中，我们提出了一个新颖的增强学习框架，旨在通过关注整个轨迹而不仅仅是最终分子构型来优化分子动力学。利用Pontryagin的最大原理（PMP）和软角色批判性（SAC）算法的随机版本，我们的框架有效地探索了非凸线分子能量景观，从而逃脱了局部最小值以稳定在低吸收状态下。我们的方法在不依赖标签数据的情况下在连续状态和动作空间中运行，使其适用于广泛的分子系统。通过对六个不同的分子（包括心动激素和催产素）进行的广泛实验，我们针对其他基于无监督的物理学方法（例如贪婪和基于Nemo的算法）展示了竞争性能。我们的方法的适应性和专注于动态轨迹优化，使其适用于在药物发现和分子设计等领域的应用。
In this paper, we present a novel reinforcement learning framework designed to optimize molecular dynamics by focusing on the entire trajectory rather than just the final molecular configuration. Leveraging a stochastic version of Pontryagin's Maximum Principle (PMP) and Soft Actor-Critic (SAC) algorithm, our framework effectively explores non-convex molecular energy landscapes, escaping local minima to stabilize in low-energy states. Our approach operates in continuous state and action spaces without relying on labeled data, making it applicable to a wide range of molecular systems. Through extensive experimentation on six distinct molecules, including Bradykinin and Oxytocin, we demonstrate competitive performance against other unsupervised physics-based methods, such as the Greedy and NEMO-based algorithms. Our method's adaptability and focus on dynamic trajectory optimization make it suitable for applications in areas such as drug discovery and molecular design.
