Mohamed Dhouioui, Jonathan Barnoud, Rhoslyn Roebuck Williams, Harry J. Stroud, Phil Bates, David R. Glowacki
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By providing an immersive 3D\nenvironment that enables visualization and manipulation of real-time molecular\nmotion, iMD-VR enables researchers and students to efficiently and intuitively\nexplore and navigate these complex, high-dimensional systems. iMD-VR platforms\noffer a unique opportunity to quickly generate rich datasets that capture human\nexperts' spatial insight regarding molecular structure and function. This paper\nexplores the possibility of employing user-generated iMD-VR datasets to train\nAI agents via imitation learning (IL). IL is an important technique in robotics\nthat enables agents to mimic complex behaviors from expert demonstrations, thus\ncircumventing the need for explicit programming or intricate reward design. We\nreview the utilization of IL for manipulation tasks in robotics and discuss how\niMD-VR recordings could be used to train IL models for solving specific\nmolecular 'tasks'. 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引用次数: 0
摘要
分子动力学模拟是研究人员在药物发现、蛋白质工程和材料设计等领域了解和设计分子结构与功能的重要计算工具。尽管分子动力学模拟很有用,但由于分子系统的高维性,其成本也很高。最近,虚拟现实中的交互式分子动力学(iMD-VR)作为一种 "人在回路中 "的策略被开发出来,它利用高性能计算加快了研究人员解决超维度采样问题的能力。iMD-VR 平台提供了一个独特的机会,可以快速生成丰富的数据集,捕捉人类专家对分子结构和功能的空间洞察力。本论文探讨了利用用户生成的 iMD-VR 数据集通过模仿学习(IL)训练人工智能代理的可能性。模仿学习是机器人领域的一项重要技术,它使人工智能代理能够模仿专家示范的复杂行为,从而避免了明确编程或复杂奖励设计的需要。我们探讨了机器人操纵任务中对 IL 的利用,并讨论了如何利用 iMD-VR 记录来训练 IL 模型,以解决特定的分子 "任务"。然后,我们研究了如何将此类方法应用于 iMD-VR 记录中捕获的数据。最后,我们概述了使用人工智能代理来增强人类高效导航构象空间的专业知识的未来研究方向和潜在挑战,并强调了这种方法如何能够在材料科学、蛋白质工程和计算机辅助药物设计等领域提供有价值的见解。
A Perspective on AI-Guided Molecular Simulations in VR: Exploring Strategies for Imitation Learning in Hyperdimensional Molecular Systems
Molecular dynamics simulations are a crucial computational tool for
researchers to understand and engineer molecular structure and function in
areas such as drug discovery, protein engineering, and material design. Despite
their utility, MD simulations are expensive, owing to the high dimensionality
of molecular systems. Interactive molecular dynamics in virtual reality
(iMD-VR) has recently been developed as a 'human-in-the-loop' strategy, which
leverages high-performance computing to accelerate the researcher's ability to
solve the hyperdimensional sampling problem. By providing an immersive 3D
environment that enables visualization and manipulation of real-time molecular
motion, iMD-VR enables researchers and students to efficiently and intuitively
explore and navigate these complex, high-dimensional systems. iMD-VR platforms
offer a unique opportunity to quickly generate rich datasets that capture human
experts' spatial insight regarding molecular structure and function. This paper
explores the possibility of employing user-generated iMD-VR datasets to train
AI agents via imitation learning (IL). IL is an important technique in robotics
that enables agents to mimic complex behaviors from expert demonstrations, thus
circumventing the need for explicit programming or intricate reward design. We
review the utilization of IL for manipulation tasks in robotics and discuss how
iMD-VR recordings could be used to train IL models for solving specific
molecular 'tasks'. We then investigate how such approaches could be applied to
the data captured from iMD-VR recordings. Finally, we outline the future
research directions and potential challenges of using AI agents to augment
human expertise to efficiently navigate conformational spaces, highlighting how
this approach could provide valuable insight across domains such as materials
science, protein engineering, and computer-aided drug design.