Home   |   Products   |   Consulting Services   |   Contact us    



  Modgraph Home
  NMRPredict Overview
  Carbon 13 NMR Prediction Overview




Neural Network Prediction - Intelligence

Examples of the intelligence added to the input layer of the NMRPredict Neural Network:

  • It is well known that para-substituents in aromatic systems have a large influence on the carbon shift. Within the HOSE code the para-substituent is 4 bonds away from the focus atom and therefore has a low priority during the selection of similar HOSE codes from the reference data collection. With the design of NMRPredict's Network a dedicated network for aromatic carbons artificially moves all 6 possible substituents on an aromtaic ring system into the first sphere, which reflects the real-world situation much better than the HOSE code.
  • When comparing the series of 'chloromethanes' starting at CH4 (Cl=0) and going to CCL4 the increase of the chemical shift values is not linearly coupled to the number of chlorines present. Therefore the number of chlorines is not a good measure for the effect to be expected on the chemical shift value. In other words, the pure number of a certain element has to be substituted by a specific non-linear input value.
  • A more complicated situation is present in a molecule like CHBr3 where the electron withdrawing effect of bromine is severly overlapped by the 'heavy-atom' effect, leading to totally unexpected values.

The examples below give a small insight into the clever design of the Neural Network within NMRPredict:

Compound HOSE Network
CI4 n.a. -286.3
CHI3 -141.0 -133.0
CH2I2 -55.0 -48.1
CHI3 -21.2 -16.4
CBr4 -28.6 -29.3
CBr2Cl2 36.9 35.5
CCl4 97.0 95.9
CCl2F2 125.7 125.6
CF4 118.8 118.6

The larger difference between HOSE and Neural Network predicted values for I compounds is based on the strong solvent dependency of this particular shiftvalue. The Br/Cl/F substitued derivatives fit together within less than 1.5 ppm.