Developing Artificial Intelligence and Deep Learning for the analysis of correlation spectroscopy data

核磁共振（NMR）光谱是一种前所未有的技术，可以在原子分辨率下获得有关类似细胞环境中大分子机器的详细信息。因此，NMR光谱已成为表征大蛋白质、发现新分子相互作用以及发现新药物先导物的必要工具。大型蛋白质和大分子机器包含数千个原子。高维（3D、4D、.）因此，需要NMR光谱，以分离单个原子的NMR信号，并促进大蛋白质的表征。高维NMR光谱的一个常见障碍是获得这些光谱所需的时间，因为基本上100 × 100正方形（3D光谱）或100 × 100 × 100立方体（4D光谱）内的每个点都需要1D NMR光谱。这使得它非常耗时，几乎不可能获得大蛋白质的高维（> 3D）光谱。在拟议的项目中，我们将利用人工智能（AI）和深度学习的巨大力量和优势，快速获取高维NMR光谱，以识别大分子机器。我们将开发一种新的工具，设计和训练一个新的深度神经网络，以便比使用传统工作流程更快地提取所需的大分子信息，因为只有一小部分点被记录下来。对于4D光谱，仅需要对100x100x100立方体内的约1%的点进行采样。这是可能的，因为NMR光谱学的理论背景是如此之好，可以很容易地生成足够的训练数据来训练神经网络。这些新工具以深度学习和人工智能为基础，不仅可以快速准确地表征分子相互作用，还可以促进超高维NMR，从而实现全新的NMR风险，甚至可以表征更大的分子机器。

Nuclear magnetic resonance (NMR) spectroscopy is an unprecedented technique to obtain detailed information - at atomic resolution - about macromolecular machines in an environment similar to the cell. NMR spectroscopy has therefore become an imperative tool for the characterisation of large proteins, for the discovery of new molecular interactions, and also for the discovery of new drug-leads. Large proteins and macromolecular machines contain thousands of atoms. High-dimensional (3D, 4D, ..) NMR spectra are therefore required in order to separate the NMR signals for the individual atoms and to facilitate a characterisation large proteins. A common hurdle with high-dimensional NMR spectra is the time required to obtain these, because essentially a 1D NMR spectrum is required for each point within a 100x100 square (3D spectra) or within a 100x100x100 cube (4D spectra). This makes it very time-consuming and nearly impossible to obtain high-dimensional (> 3D) spectra for large proteins. During the proposed project we will leverage the immense power and strength of Artificial Intelligence (AI) and Deep Learning to allow for fast acquisition of high-dimensional NMR spectra to characterise macromolecular machines. We will develop a new tool, where a new deep neural network will be designed and trained so that the required information about the macromolecule can be extracted orders of magnitude faster than using the traditional workflow because only a fraction of the points are recorded. For 4D spectra only about 1% of the points within the 100x100x100 cube need to be sampled. This is possible because the theoretical background of NMR spectroscopy is so well defied that sufficient training data easily can be generated to train the neural networks. The new tools, anchored in deep learning and AI, will not only allow for fast and accurate characterisation of molecular interactions but will also facilitate ultra-high-dimensional NMR that will allow for completely new NMR ventures and for even larger molecular machines to be characterised.