Prediction of chemical shift in NMR: A review

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.

Analytical and Structural Tools of Lipid Hydroperoxides: Present State and Future Perspectives

“… Incorporation of the numerous literature 1 H and 13 C-NMR chemical shift and coupling constant data of lipid hydroperoxides into open-source NMR (web) databases. These data combined with prediction software will greatly facilitate structural information from 1 H, 13 C, 1 H- 1 H COSY, 1 H- 1 H TOCSY, 1 H- 13 C HSQC, and 1 H- 13 C HMBC NMR data [ 194 , 195 , 196 , 197 ]. DFT calculations of 1 H-, and 13 C- chemical shifts and J couplings can contribute significantly to the unequivocal resonance assignment, identification of cis / trans geometric isomers, and diastereomeric pairs of complex hydroperoxides and solvent effects [ 142 , 143 , 144 , 145 , 146 , 147 , 148 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 , 198 , 199 ].…”

“…Incorporation of the numerous literature 1 H and 13 C-NMR chemical shift and coupling constant data of lipid hydroperoxides into open-source NMR (web) databases. These data combined with prediction software will greatly facilitate structural information from 1 H, 13 C, 1 H- 1 H COSY, 1 H- 1 H TOCSY, 1 H- 13 C HSQC, and 1 H- 13 C HMBC NMR data [ 194 , 195 , 196 , 197 ].…”

Application of 1,1‐ADEQUATE and DFT to correct13C misassignments of carbonyl chemical shifts for carbapenem antibiotics

“…Thus, this strongly indicates that these chemical shift assignments need to be reversed, similar to meropenem and imipenem. Furthermore, a large error of 10.1 ppm for the β-lactam carbonyl (C-7) was observed by the hierarchically ordered spherical description of environment (HOSE) code-based [30,31] method implemented in ACD Labs (see Supporting Information). [32] This error appears to be based on the fact that the original incorrect assignments for imipenem are used in the ACD Labs database, and imipenem has a very similar structure to thienamycin, leading to a high weighting factor by the algorithm.…”

Regression Machine Learning Models Used to Predict DFT-Computed NMR Parameters of Zeolites

“…Hitherto, several ML models [12] have been built and applied for predicting NMR isotropic shielding (σ iso ) or, respectively, the chemical shift (δ = σ re f − σ iso ) of 1 H, 13 C, 13 O, and 13 N nuclei in small organic, aromatic molecules or molecular crystals [2,6,[13][14][15][16][17][18][19][20]. These ML models comprise deep neural networks (DNNs) [15], convolutional neural networks (CNNs) [16], the IMPRESSION model based on kernel-ridge regression (KRR) [6,19,20], linear-ridge regression [2], gradient boosting regression (GBR) [21,22], graph neural networks (GNNs) [23,24], and the ∆-ML method [7].…”