Modgraph NMR Predict Desktop

Mnova integrates a fast simulation module of 1H and 13C-NMR spectra, called Modgraph NMRPredict Desktop.

See also 'How to predict a spectrum with NMRPredict Desktop?

NMRPredict Desktop is faster than NMRPredict server-based, because it does not need to log in to a server to predict a spectrum. Besides, it makes use of the fastest prediction algorithms (Neural Network, Increments and Best) implemented into NMRPredict. Other very significant advantages are that the software can be used without an internet connection, there are no access issues with firewalls, and no risk to confidential data being sent over the internet.

NMRPredict Desktop uses a Neural Network system for the prediction of 13C-NMR spectra, and the NMR tables (increments methodology) of Professor Ernö Pretsch (ETH, Zürich) for a fast simulation mode of 1H and X-nuclei: 11B, 15N, 17O, 19F, 29Si, 31P  NMR spectra.

NMR Predict Desktop also includes the calculation of heteronuclear (HF, HP, CF and CP) couplings.

The Best algorithm is the combined approach that is capable of producing significantly improved proton NMR predictions for the data set upon which it has been tested down to below the 0.2 ppm target error.

You will find further information about this feature in:

http://www.modgraph.co.uk/best_proton_press_release.htm

Please bear in mind that we are not giving any guarantees that the “Best” will always give prediction accuracy as low as 0.18 ppm.  Statistically this is what we got against the 1.1 million shift values in the 90,000 data we used.  This may or may not be the case with user data. 

The Neural Network algorithm is much more general and error tolerant than the HOSE code approach (based on a reference spectra database) and is much more accurate at predicting shifts not found in the database.

The Neural Network prediction method in NMRPredict Desktop was developed during the mid 1990s in the group of Professor Robien at the University of Vienna. It was developed by V Purtuc and gave a very broad application range to cover general organic chemistry.

The basic design principles as defined in 1995 were:

•to be general enough and be able to handle problems from basic organic chemistry to complex natural products.

•to be able to handle "unusual" organic chemistry like organometallics.

•to be solvent specific during prediction.

•to be able to use stereochemical information not restricted by ring size, in the same way as could be done within the HOSE code technology.

•to make sure that only interpolation and not extrapolation occured when making predictions.

The Neural Network implemented into NMRPredict Desktop has been thoroughly tested and proved to be both reliable and accurate. The 4,000,000 assigned chemical shift values of the available 345,000 reference spectra can be predicted with an average deviation between experimental versus calculated of below 2.00 ppm. This includes compounds which are not well handled by traditional prediction programs, such as ferrocenes and chromium complexes.