Reinforcement Learning for Molecular Dynamics Optimization: A Stochastic Pontryagin Maximum Principle Approach
University of Texas at Austin（德克萨斯大学奥斯汀分校）
Chandrajit Bajaj, Minh Nguyen, Conrad Li
备注：
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
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.
A Pontryagin Method of Model-based Reinforcement Learning via Hamiltonian Actor-Critic
Information Hub, HKUST (Guangzhou)（香港科技大学（广州）信息枢纽）；Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）；Department of Electrical and Computer Engineering, University of Alberta（阿尔伯塔大学电气与计算机工程系）
Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning
University of Göttingen（哥廷根大学）；University of Toronto（多伦多大学）；Vector Institute for Artificial Intelligence（向量人工智能研究所）；Canadian Institute for Advanced Research（加拿大高等研究院）
Scalable Fragment-Based 3D Molecular Design with Reinforcement Learning
University of Toronto（多伦多大学）；Vector Institute for Artificial Intelligence（向量人工智能研究院）；Harvard University（哈佛大学）；Canadian Institute for Advanced Research（加拿大高级研究院）
Guiding Diffusion Models with Reinforcement Learning for Stable Molecule Generation
Fudan University（复旦大学）；Shanghai Innovation Institute（上海创新研究院）；Shanghai Academy of Artificial Intelligence for Science（上海人工智能科学研究院）；INFTECH；Zhongshan Hospital（中山医院）
Fine-Tuning Diffusion Models for Molecular Generation via Reinforcement Learning and Fast Sampling
Department of Computer Science and Engineering, Shanghai Jiao Tong University（上海交通大学计算机科学与工程系）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东人工智能与数字经济实验室（深圳））
QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning
Department of Computer Science（计算机科学系）；University of Birmingham（伯明翰大学）；QunaSys Europe（QunaSys欧洲分公司）；Hon Hai Research Institute（鸿海研究机构）；Departement of Computer Science（计算机科学系）
Agentic reinforcement learning empowers next-generation chemical language models for molecular design and synthesis
School of Electronic and Computer Engineering, Peking University（电子与计算机工程学院，北京大学）；Shenzhen Graduate School, Shenzhen, 518055, China（深圳研究生院，深圳，518055，中国）；International Digital Economy Academy (IDEA)（国际数字经济学院（IDEA））；Peng Cheng Laboratory, Shenzhen, 518000, China（鹏城实验室，深圳，518000，中国）
BindGPT: A Scalable Framework for 3D Molecular Design via Language Modeling and Reinforcement Learning
Insilico Medicine Canada Inc.（英矽智能加拿大公司）；Mila – Quebec AI Institute（米拉-魁北克人工智能研究所）；Polytechnique Montréal（蒙特利尔工业大学）；Insilico Medicine AI Limited（英矽智能人工智能有限公司）；CIFAR AI Chair（加拿大高级研究院人工智能主席项目）
MolEditRL: Structure-Preserving Molecular Editing via Discrete Diffusion and Reinforcement Learning
Artificial Intelligence Thrust, Hong Kong University of Science and Technology (Guangzhou)（香港理工大学（广州）人工智能研究方向）；College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（南京航空航天大学计算机科学与技术学院）
MolHop: Scaffold-Hopping Molecular Design with Agentic Reinforcement Learning
State Key Laboratory of Physical Chemistry of Solid Surface, College of Chemistry and Chemical Engineering, Xiamen University（厦门大学化学化工学院固体表面物理化学国家重点实验室）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Institute of Artificial Intelligence, Xiamen University（厦门大学人工智能研究院）
A Reinforcement Learning-Driven Transformer GAN for Molecular Generation
D3 Center（D3中心）；Osaka University（大阪大学）；Graduate School of Advanced Science and Engineering（先进科学与工程研究生院）；Hiroshima University（广岛大学）；Centre for Cyber Resilience and Trust（网络韧性与信任中心）；Deakin University（德金大学）；Graduate School of Informatics（信息科学研究生院）；Nagoya University（名古屋大学）
Quantum-inspired Reinforcement Learning for Synthesizable Drug Design
Rensselaer Polytechnic Institute (RPI)（伦斯勒理工学院）；University of Illinois Urbana-Champaign (UIUC)（伊利诺伊大学厄巴纳-香槟分校）；Stanford University（斯坦福大学）；Virginia Tech（弗吉尼亚理工大学）
Widely Used and Fast De Novo Drug Design by a Protein Sequence-Based Reinforcement Learning Model
West China Hospital, Sichuan University（四川大学华西医院）；University of Gothenburg（哥德堡大学）；The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital)（中国科学院大学附属肿瘤医院（浙江省肿瘤医院））；Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences（中国科学院基础医学与肿瘤研究所（IBMC））
Optimal Control of Probabilistic Dynamics Models via Mean Hamiltonian Minimization
Department of Machine Learning and Neural Computing, Donders Institute for Brain, Cognition and Behaviour, Radboud University（机器学习与神经计算系，多纳尔斯脑认知行为研究所，拉德堡德大学）；Cluster of Excellence Machine Learning, University of Tübingen（卓越机器学习集群，图宾根大学）；Department of Engineering, Control Group, University of Oxford（工程系，控制组，牛津大学）
Learning to grow: control of material self-assembly using evolutionary reinforcement learning
Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；National Research Council of Canada（加拿大国家研究委员会）；Vector Institute for Artificial Intelligence（矢量人工智能研究所）
Reinforced dynamics for enhanced sampling in large atomic and molecular systems
Princeton University（普林斯顿大学）；Institute of Applied Physics and Computational Mathematics（应用物理与计算数学研究所）；CAEP Software Center for High Performance Numerical Simulation（中国工程物理研究院高性能数值模拟软件中心）；Beijing Institute of Big Data Research（北京大数据研究院）
