![]() 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. ![]() Methods to automate structure elucidation that can be applied broadly across chemical structure space have the potential to greatly accelerate chemical discovery.
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