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The IMPRESSION (Intelligent Machine PREdiction of Shift and ScalarInformation Of Nuclei) machine learning system provides an efficient andaccurate route to the prediction of NMR parameters from 3-dimensional chemicalstructures. Here we demonstrate that machine learning predictions, trained onquantum chemical computed values for NMR parameters, are essentially asaccurate but computationally much more efficient (tens of milliseconds permolecule) than quantum chemical calculations (hours/days per molecule).Training the machine learning systems on quantum chemical, rather thanexperimental, data circumvents the need for existence of large, structurallydiverse, error-free experimental databases and makes IMPRESSION applicable tosolving 3-dimensional problems such as molecular conformation and isomerism

IMPRESSION -- Prediction of NMR Parameters for 3-dimensional chemical structures using Machine Learning with near quantum chemical accuracy