Abstract：
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 empirical) methods leverage databases of known parameter values to estimate parameters for unknown or novel molecules. This is in contrast to popular ab initio techniques that 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.
摘要：
溶液状态NMR参数的计算，包括化学位移值和标量耦合常数，通常是明确结构分配的关键步骤。数据驱动（有时称为经验）方法利用已知参数值的数据库来估计未知或新颖分子的参数。这与流行的从头算技术形成对比，后者使用详细的量子计算化学计算来得出参数估计。数据驱动的方法有可能比abinito技术快得多，并且在过去十年中随着高质量的NMR参数数据库和新颖的机器学习方法的兴起而重新引起了人们的兴趣。这里,我们回顾了这些方法，他们的优势和陷阱，以及它们所建立的数据库。
