Several approaches to simulate NMR observables of carbohydrates has been reviewed. They include:
*Universal statistical database approaches (ACDLabs, Modgraph, etc.)
*Usage of neural networks to refine the predictions
*Regression based methods
*CHARGE
*Carbohydrate-optimized empirical schemes (CSDB/BIOPSEL, CASPER).
*Combined molecular mechanics/dynamics geometry calculation and quantum-mechanical simulation/iteration of NMR observables (PERCH NMR Software)
*ONIOM approaches (optimization of different parts of molecule with different accuracy)
*Ab initio calculations.
Growing computational power allows usage of thorough quantum-mechanical calculations at high theory levels and large basis sets for refining the molecular geometry of carbohydrates and subsequent prediction of NMR observables using GIAO and other methods with or without solvent effect account. Among combinations of theory level and a basis set reported as sufficient for NMR predictions were B3LYP/6-311G++(2d,2p) and PBE/PBE (see review). It was shown for saccharides that carbohydrate-optimized empirical schemes provide significantly better accuracy (0.0-0.5 ppm per C resonance) than quantum chemical methods (above 2.0 ppm per resonance) reported as best for NMR simulations, and work thousands times faster. However, these methods can predict only chemical shifts and perform poor for non-carbohydrate parts of molecules.
As a representative example, see figure on the right.