DeepNMR: Unleashing the full potential of NMR spectroscopy with artificial intelligence and deep learning

DeepNMR：通过人工智能和深度学习释放 NMR 波谱的全部潜力

• 批准号: EP/X036782/1
• 负责人: Flemming Hansen
• 金额: $ 274.36万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX036782%2F1
• 关键词: DeepNMR; Unleashing; full; potential; NMR

Nuclear Magnetic Resonance (NMR) spectroscopy is ubiquitous in material science, chemistry, structural biology, and clinicaldiagnosis. In chemical synthesis, the identification and characterisation of compounds hinge on NMR and in bioscience NMR providesunprecedented insight into functional motions and on non-covalent interactions with atomic resolution. However, the analysis ofNMR spectra, in particular biomolecular NMR spectra, still largely depend on interpretations by specialists with years of training. Evenmore so, the development of NMR methods to allow for new applications relies on specialists with decades of training and excellentintuition. These constraints have meant that the full potential of NMR as a tool in chemistry, biochemistry, and medicine, is far frombeing reached. The proposed research will address this inhibitory constrain of biomolecular NMR by fully integrating artificialintelligence (AI) with the analysis of NMR data and with the development of new NMR methods. Using supervised deep learning,deep neural networks (DNNs) will be developed to analyse complex biomolecular NMR spectra. Analysis with DNNs is robust andonce the DNN is trained, it does not require an optimisation of processing parameters. The DNNs can therefore easily be integratedinto automated data-processing pipelines. Reinforcement deep learning will be employed to design intelligent machines that providethe next generations of NMR methods. With these tools, the scientist can simply request the intelligent machine to derive a methodand an analysis tool to characterise a specific set of parameters or functions of the macromolecule in question. Being able to fullyintegrate AI with NMR, and concomitantly develop NMR and AI as one tool, is high-risk, but once successful will unleash the immensepotential of current and future NMR hardware to provide unprecedented insights into a broad range of molecules, in material science,in biochemistry, and in medicine.

核磁共振波谱学在材料科学、化学、结构生物学和临床诊断中无处不在。在化学合成中，化合物的鉴定和表征取决于核磁共振，而在生物科学中，核磁共振提供了对功能运动和与原子分辨率的非共价相互作用的前所未有的见解。然而，核磁共振光谱的分析，特别是生物分子核磁共振光谱的分析，在很大程度上仍然依赖于经过多年培训的专家的解释。更重要的是，核磁共振方法的发展，以允许新的应用依赖于专家与数十年的培训和出色的直觉。这些限制意味着核磁共振作为化学、生物化学和医学工具的全部潜力还远远没有实现。拟议的研究将通过充分整合人工智能（AI）与核磁共振数据分析和新核磁共振方法的开发来解决生物分子核磁共振的这种抑制约束。利用监督深度学习，深度神经网络（dnn）将被开发用于分析复杂的生物分子核磁共振光谱。DNN的分析是鲁棒的，一旦DNN被训练，它不需要优化处理参数。因此，深度神经网络可以很容易地集成到自动数据处理管道中。强化深度学习将被用于设计提供下一代核磁共振方法的智能机器。有了这些工具，科学家可以简单地要求智能机器推导出一种方法和分析工具来表征所讨论的大分子的一组特定参数或功能。能够将人工智能与核磁共振完全集成，并同时开发核磁共振和人工智能作为一个工具，是高风险的，但一旦成功，将释放当前和未来核磁共振硬件的巨大潜力，为广泛的分子，材料科学，生物化学和医学提供前所未有的见解。