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