how to predict nmr spectra in mestrenova 6

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

How to predict nmr spectra in mestrenova 6