A Perspective on AI-Guided Molecular Simulations in VR: Exploring Strategies for Imitation Learning in Hyperdimensional Molecular Systems

• URL: http://arxiv.org/abs/2409.07189v1
• Date: Wed, 11 Sep 2024 11:21:02 GMT
• Title: A Perspective on AI-Guided Molecular Simulations in VR: Exploring Strategies for Imitation Learning in Hyperdimensional Molecular Systems
• Authors: Mohamed Dhouioui, Jonathan Barnoud, Rhoslyn Roebuck Williams, Harry J. Stroud, Phil Bates, David R. Glowacki,
• Abstract summary: Interactive molecular dynamics in virtual reality (iMD-VR) has recently been developed as a 'human-in-the-loop' strategy. This paper explores the possibility of employing user-generated iMD-VR datasets to train AI agents via imitation learning (IL)
• Score: 0.7853804618032806
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• MatPilot: an LLM-enabled AI Materials Scientist under the Framework of Human-Machine Collaboration [13.689620109856783]
  We developed an AI materials scientist named MatPilot, which has shown encouraging abilities in the discovery of new materials. The core strength of MatPilot is its natural language interactive human-machine collaboration. MatPilot integrates unique cognitive abilities, extensive accumulated experience, and ongoing curiosity of human-beings.
  arXiv  Detail & Related papers  (2024-11-10T12:23:44Z)
• Empowering Biomedical Discovery with AI Agents [15.125735219811268]
  We envision "AI scientists" as systems capable of skeptical learning and reasoning. Biomedical AI agents combine human creativity and expertise with AI's ability to analyze large datasets. AI agents can impact areas ranging from virtual cell simulation, programmable control of phenotypes, and the design of cellular circuits to developing new therapies.
  arXiv  Detail & Related papers  (2024-04-03T16:08:01Z)
• Virtual Reality for Understanding Artificial-Intelligence-driven Scientific Discovery with an Application in Quantum Optics [1.0858565995100633]
  We show how transferring part of the analysis process into an immersive Virtual Reality environment can assist researchers in developing an understanding of AI-generated solutions. We demonstrate the usefulness of VR in finding interpretable configurations of abstract graphs, representing Quantum Optics experiments.
  arXiv  Detail & Related papers  (2024-02-20T17:48:01Z)
• On the Emergence of Symmetrical Reality [51.21203247240322]
  We introduce the symmetrical reality framework, which offers a unified representation encompassing various forms of physical-virtual amalgamations. We propose an instance of an AI-driven active assistance service that illustrates the potential applications of symmetrical reality.
  arXiv  Detail & Related papers  (2024-01-26T16:09:39Z)
• Agent AI: Surveying the Horizons of Multimodal Interaction [83.18367129924997]
  "Agent AI" is a class of interactive systems that can perceive visual stimuli, language inputs, and other environmentally-grounded data. We envision a future where people can easily create any virtual reality or simulated scene and interact with agents embodied within the virtual environment.
  arXiv  Detail & Related papers  (2024-01-07T19:11:18Z)
• Brain-Inspired Computational Intelligence via Predictive Coding [89.6335791546526]
  Predictive coding (PC) has shown promising performance in machine intelligence tasks. PC can model information processing in different brain areas, can be used in cognitive control and robotics.
  arXiv  Detail & Related papers  (2023-08-15T16:37:16Z)
• MDsrv -- visual sharing and analysis of molecular dynamics simulations [49.88123522856065]
  The MDsrv is a tool that streams MD trajectories and displays them interactively in web browsers. We have now enhanced the MDsrv to further simplify the upload and sharing of MD trajectories. An important innovation is that the MDsrv can now access and visualize trajectories from remote datasets.
  arXiv  Detail & Related papers  (2022-03-25T14:08:24Z)
• DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local Explanations [119.1953397679783]
  We focus on advancing the state-of-the-art in interpreting multimodal models. Our proposed approach, DIME, enables accurate and fine-grained analysis of multimodal models.
  arXiv  Detail & Related papers  (2022-03-03T20:52:47Z)
• Multi-Agent Simulation for AI Behaviour Discovery in Operations Research [0.9137554315375919]
  ACE0 is a platform for evaluating the suitability and viability of AI methods for behaviour discovery in multiagent simulations. We provide a technical overview of the system architecture, describe a case study of behaviour discovery in the aerospace domain, and provide a qualitative evaluation of the system.
  arXiv  Detail & Related papers  (2021-08-30T15:14:06Z)
• Artificial Intelligence based Autonomous Molecular Design for Medical Therapeutic: A Perspective [9.371378627575883]
  Domain-aware machine learning (ML) models have been increasingly adopted for accelerating small molecule therapeutic design. We present the most recent breakthrough achieved by each of the components, and how such autonomous AI and ML workflow can be realized.
  arXiv  Detail & Related papers  (2021-02-10T00:43:46Z)
• ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation [75.0278287071591]
  ThreeDWorld (TDW) is a platform for interactive multi-modal physical simulation. TDW enables simulation of high-fidelity sensory data and physical interactions between mobile agents and objects in rich 3D environments. We present initial experiments enabled by TDW in emerging research directions in computer vision, machine learning, and cognitive science.
  arXiv  Detail & Related papers  (2020-07-09T17:33:27Z)

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.