Prediction of chemical shift in NMR: a review

Calculation of solution-state NMR parameters, including chemical shift values and scalar coupling constants, is often a crucial step for unambiguous structure assignment. Data-driven (sometimes called \textit{empirical}) methods leverage databases of known parameter values to estimate parameters for unknown or novel molecules. This is in contrast to popular \textit{ab initio} techniques which use detailed quantum computational chemistry calculations to arrive at parameter estimates. Data-driven methods have the potential to be considerably faster than ab inito techniques and have been the subject of renewed interest over the past decade with the rise of high-quality databases of NMR parameters and novel machine learning methods. Here we review these methods, their strengths and pitfalls, and the databases they are built on.