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However, these programs still require a large amount of human intervention to pick out the relevant peaks from complex 2D NMR spectra. 8–18 However, the inverse problem of automated prediction of the structure of a molecule from its NMR spectrum is much more challenging because of the enormity of molecular structure space.Ĭomputer-assisted structure elucidation (CASE) programs have been developed to help interpret 2D and 1D NMR spectra. database similarity searches 4 or additivity rules 5–7), and machine learning (ML) models. The forward problem of automated prediction of NMR peak shifts and splittings for a given molecule has seen much success using ab initio calculations, 1–3 simple empirical methods ( e.g. In practice, chemists thus frequently resort to the use of 2D NMR experiments to deduce structures from complex spectra, at the expense of considerable additional time and resources. Even relatively small molecules may have a large number of 1H NMR peaks with complex splitting patterns, which are often obscured by peak overlaps. While sample preparation and data collection for routine 1D NMR experiments are facile, data interpretation is often time-consuming and error-prone. NMR spectra encode the local environments of the atoms that make up a molecule, providing molecular “fingerprints” that can be used to deduce connectivity and relative stereochemistry. NMR is the most widely used technique for characterizing the structure of organic molecules.
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Methods to automate structure elucidation that can be applied broadly across chemical structure space will therefore aid researchers in accelerating chemical discovery and will ultimately prove essential for creating fully autonomous molecular and reaction discovery platforms. As advances are made in automating the planning and execution of experiments, this bottleneck will become more acute because the rate at which previously unknown compounds are generated will increase. Introduction Solving the structure of unknown compounds is a major bottleneck in the chemical sciences that limits the pace of molecular and reaction discovery for innumerable applications. This advance will aid in solving the structure of unknown compounds, and thus further the development of automated structure elucidation tools that could enable the creation of fully autonomous reaction discovery platforms. Using experimental spectra and molecular formulae for molecules containing up to 10 non-hydrogen atoms, the correct constitutional isomer was the highest-ranking prediction made by our model in 67.4% of the cases and one of the top-ten predictions in 95.8% of the cases. In particular, our ML-based algorithm takes input NMR spectra and (i) predicts the presence of specific substructures out of hundreds of substructures it has learned to identify (ii) annotates the spectrum to label peaks with predicted substructures and (iii) uses the substructures to construct candidate constitutional isomers and assign to them a probabilistic ranking. Here we introduce a machine learning (ML) framework that provides a quantitative probabilistic ranking of the most likely structural connectivity of an unknown compound when given routine, experimental one dimensional 1H and/or 13C NMR spectra. NMR spectroscopy is the most widely used and arguably the most powerful method for elucidating structures of organic molecules.
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Methods to automate structure elucidation that can be applied broadly across chemical structure space have the potential to greatly accelerate chemical discovery.