Reinforcement Learning for Molecular Dynamics Optimization: A Stochastic Pontryagin Maximum Principle Approach

Autor:	Bajaj, Chandrajit, Nguyen, Minh, Li, Conrad
Rok vydání:	2022
Předmět:	Mathematics - Optimization and Control Computer Science - Artificial Intelligence Computer Science - Machine Learning Mathematics - Functional Analysis 37N40 K.3.2 I.2.8
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted to the International Conference on Neural Information Processing (ICONIP) 2024. To be published in Springer-Nature Communications in Computer and Information Science (CCIS) Series
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2212.03320 Zobrazit plný text záznamu View this record from Arxiv