A single NMR spectrum of a large biomolecule can take a specialist hours or days to interpret, and designing new NMR methods requires decades of expertise. This project will train artificial intelligence to do both tasks automatically. NMR spectroscopy is a workhorse technique across chemistry, structural biology, and medicine, used to identify chemical compounds and reveal how proteins move and interact. But its full power remains untapped because analysing complex biomolecular spectra—and inventing new ways to acquire them—still depends on a tiny pool of highly trained specialists. The bottleneck is human intuition, not the hardware. The researchers will build two types of deep neural networks. One set will analyse spectra robustly without requiring manual parameter tweaking, slotting directly into automated pipelines. The other, using reinforcement learning, will act as an intelligent method designer: a scientist could simply request a new NMR technique to measure a specific molecular property, and the machine would derive both the method and the analysis tool. If successful, this high-risk integration of AI with NMR could unlock the full potential of existing and future spectrometers, providing faster, deeper insights into molecules for materials science, biochemistry, and clinical diagnostics.
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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.
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