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

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