NMR Predict

Accurate prediction of 1H and 13C NMR spectra from a chemical structure.

Prediction of chemical shifts of other nuclides is also available.

Mnova NMR Predict: 45-day FREE trial

product_icon_download 1. Download

A plugin integrated in Mnova (separate license). No extra installer is required.

product_icon_install 2. Installation

Open Mnova and go to ‘Help/Get-Install Licenses’. Select ‘Evaluate’.

product_icon_license 3. License

Fill in the form to receive your trial license via e-mail.



Make better decisions for your spectra faster!

  • Compute and display accurate chemical shifts for 1H, 13C, and other nuclides (11B, 15N,17O, 19F, 29Si, 31P) and scalar coupling constants.
  • Simulation of 1H-NMR spectra are carried out using a rigorous quantum mechanism approach that takes into account strong coupling effects.
  • Prediction algorithms are based on traditional HOSE code methods as well as on state-of-the-art machine-learning techniques (i.e. neural networks, random forests and partial least squares).
  • If the experimental spectrum is available, prediction will use the same experimental conditions (e.g. solvent and spectral properties: spectral width, spectrometer frequency, chemical shift reference, digital number of points, etc.)
  • Train your predictions by building your NMR databases from already assigned molecular structures.



13C NMR Prediction

Prediction of 13C NMR chemical shifts is carried out in Mnova NMRPredict using two different procedures which are then combined by means of the so called ‘Best’ prediction.

The first one is a chemical shift prediction orientated database. This is done with an extended HOSE code method (Hierarchically Ordered Spherical of Environment). It consists of a one dimensional coding of the chemical environment of each carbon atom. The HOSE code approach works very well for query structures that are well represented in the reference collection. Starting from the atom of interest, all atoms bonded directly to this atom (first sphere), over two bonds (second sphere) – and so on – are coded using characters which define atom types, bond types, ring closures, and spheres.

‘Best’ prediction also uses a Neural Network algorithm which is more error tolerant than the HOSE code approach. It gives more accurate results when the query atom is not represented in the database.

1H NMR prediction

This prediction follows a similar approach to the case of 13C spectra. First, a prediction algorithm that is based on tabulated chemical shifts for classes of structures, corrected with additive contributions from neighboring functional groups or substructures, is carried out. These substructures provide the base value of a final predicted chemical shift. Furthermore, a complementary prediction approach based upon partial atomic charges and steric interactions is also performed.

This algorithm, named CHARGE, is a composite program made up of a neural network based approach for the one-, two- and three-bond substituent effects plus a theoretical calculation of the long range effects of substituents. This method requires first the generation of 3D conformers from a 2D structure so the individual spectra of all conformers are predicted. Finally, an average predicted spectrum is calculated (employing a Boltzmann weighted average of the shifts calculated for all low-energy conformers).

1H NMR ‘Best‘ prediction analyses the individual chemical shifts from the two complementary methods to give a single, unified predicted chemical shift.

Academic, Government & Industrial


  • Pharmaceutical, chemical and food industries and QC environments
  • Research and NMR teaching in Academia
  • Suitable for individual users, research groups as well as large institutions and companies
Washington University
West Virginia University