NMR landscapes for chemical shift prediction

The ability to reliably predict NMR chemical shifts plays an important role in elucidating the structure of organic molecules. Additionally, an intriguing question is how the multitude of variable factors (structural, electronic, and environmental) correlate with the actual electromagnetic shielding effect that determines the chemical shift value. This work presents NMRscape as a new tool for understanding these correlations by constructing the landscape that describes the relationship between the chemical shift value and the moieties bonded to a molecular scaffold. The scaffold may be as small as a single atom probed by NMR or a larger molecular framework containing the probed atom. NMRscape operates with only a list of the chemical moieties bonded to the scaffold, without utilizing any potentially biasing chemometric descriptors. The corresponding chemical shift landscape is constructed based on fundamental physical principles, which makes NMRscape a credible chemical shift prediction and analysis tool. As an illustration, we demonstrate that NMRscape can predict ¹³C chemical shifts with an accuracy exceeding the substituent chemical shift (SCS) increment, hierarchical organization of spherical environments (HOSE), and neural networks (NN), methods for three distinct families of molecules sharing a common scaffold structure with moieties placed at two variable sites. The constructed NMR landscapes confirmed known empirical rules relating chemical shift values to the variation of chemical moieties on a scaffold, as well as uncovered hitherto hidden relationships. The practical importance of NMRscape is discussed.