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

• URL: http://arxiv.org/abs/2212.03320v2
• Date: Mon, 21 Oct 2024 06:46:52 GMT
• Title: Reinforcement Learning for Molecular Dynamics Optimization: A Stochastic Pontryagin Maximum Principle Approach
• Authors: Chandrajit Bajaj, Minh Nguyen, Conrad Li,
• Abstract summary: We present a novel reinforcement learning framework designed to optimize molecular dynamics. Our framework focuses on the entire trajectory rather than just the final molecular configuration. Our method makes it suitable for applications in areas such as drug discovery and molecular design.
• Score: 3.0077933778535706
• License:
• Abstract: 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.

Related papers

• Conditional Latent Space Molecular Scaffold Optimization for Accelerated Molecular Design [17.175846006359674]
  We introduce Conditional Latent Space Molecular Scaffold Optimization (CLaSMO) to modify molecules strategically while maintaining similarity to the original input. Our LSBO setting improves the sample-efficiency of our optimization, and our modification approach helps us to obtain molecules with higher chances of real-world applicability. We also provide an open-source web application that enables chemical experts to apply CLaSMO in a Human-in-the-Loop setting.
  arXiv  Detail & Related papers  (2024-11-03T03:17:38Z)
• Quantum-inspired Reinforcement Learning for Synthesizable Drug Design [20.00111975801053]
  We introduce a novel approach using the reinforcement learning method with quantum-inspired simulated annealing policy neural network to navigate the vast discrete space of chemical structures intelligently. Specifically, we employ a deterministic REINFORCE algorithm using policy neural networks to output transitional probability to guide state transitions and local search. Our methods are evaluated with the Practical Molecular Optimization (PMO) benchmark framework with a 10K query budget.
  arXiv  Detail & Related papers  (2024-09-13T20:43:16Z)
• Aligning Target-Aware Molecule Diffusion Models with Exact Energy Optimization [147.7899503829411]
  AliDiff is a novel framework to align pretrained target diffusion models with preferred functional properties. It can generate molecules with state-of-the-art binding energies with up to -7.07 Avg. Vina Score.
  arXiv  Detail & Related papers  (2024-07-01T06:10:29Z)
• Active learning of Boltzmann samplers and potential energies with quantum mechanical accuracy [1.7633275579210346]
  We develop an approach combining enhanced sampling with deep generative models and active learning of a machine learning potential. We apply this method to study the isomerization of an ultrasmall silver nanocluster, belonging to a set of systems with diverse applications in the fields of medicine and biology.
  arXiv  Detail & Related papers  (2024-01-29T19:01:31Z)
• D4FT: A Deep Learning Approach to Kohn-Sham Density Functional Theory [79.50644650795012]
  We propose a deep learning approach to solve Kohn-Sham Density Functional Theory (KS-DFT) We prove that such an approach has the same expressivity as the SCF method, yet reduces the computational complexity. In addition, we show that our approach enables us to explore more complex neural-based wave functions.
  arXiv  Detail & Related papers  (2023-03-01T10:38:10Z)
• Statistically Optimal Force Aggregation for Coarse-Graining Molecular Dynamics [55.41644538483948]
  coarse-grained (CG) models have the potential to simulate large molecular complexes beyond what is possible with atomistic molecular dynamics. A widely used methodology for learning CG force-fields maps forces from all-atom molecular dynamics to the CG representation and matches them with a CG force-field on average. We show that there is flexibility in how to map all-atom forces to the CG representation, and that the most commonly used mapping methods are statistically inefficient and potentially even incorrect in the presence of constraints in the all-atom simulation.
  arXiv  Detail & Related papers  (2023-02-14T14:35:39Z)
• Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics [15.660348943139711]
  We leverage connections between score-based generative models, force fields and molecular dynamics to learn a CG force field without requiring any force inputs during training. While having a vastly simplified training setup compared to previous work, we demonstrate that our approach leads to improved performance across several small- to medium-sized protein simulations.
  arXiv  Detail & Related papers  (2023-02-01T17:09:46Z)
• Guaranteed Conservation of Momentum for Learning Particle-based Fluid Dynamics [96.9177297872723]
  We present a novel method for guaranteeing linear momentum in learned physics simulations. We enforce conservation of momentum with a hard constraint, which we realize via antisymmetrical continuous convolutional layers. In combination, the proposed method allows us to increase the physical accuracy of the learned simulator substantially.
  arXiv  Detail & Related papers  (2022-10-12T09:12:59Z)
• Molecular Attributes Transfer from Non-Parallel Data [57.010952598634944]
  We formulate molecular optimization as a style transfer problem and present a novel generative model that could automatically learn internal differences between two groups of non-parallel data. Experiments on two molecular optimization tasks, toxicity modification and synthesizability improvement, demonstrate that our model significantly outperforms several state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-11-30T06:10:22Z)
• Improving RNA Secondary Structure Design using Deep Reinforcement Learning [69.63971634605797]
  We propose a new benchmark of applying reinforcement learning to RNA sequence design, in which the objective function is defined to be the free energy in the sequence's secondary structure. We show results of the ablation analysis that we do for these algorithms, as well as graphs indicating the algorithm's performance across batches.
  arXiv  Detail & Related papers  (2021-11-05T02:54:06Z)
• Molecular Design in Synthetically Accessible Chemical Space via Deep Reinforcement Learning [0.0]
  We argue that existing generative methods are limited in their ability to favourably shift the distributions of molecular properties during optimization. We propose a novel Reinforcement Learning framework for molecular design in which an agent learns to directly optimize through a space of synthetically-accessible drug-like molecules.
  arXiv  Detail & Related papers  (2020-04-29T16:29:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.