Neural networks Counterpropagation

Counterpropagation neural networks (CFG NN) were then used to establish relationships between protons and their H NMR chemical shifts. A detailed description of this method is given in the Tools Section 10,2.4.2,... 

A combination of physicochemical, topological, and geometric information is used to encode the environment of a proton, The geometric information is based on (local) proton radial distribution function (RDF) descriptors and characterizes the 3D environment of the proton. Counterpropagation neural networks established the relationship between protons and their h NMR chemical shifts (for details of neural networks, see Section 9,5). Four different types of protons were... 

Figure 10.2-9. Application of a counterpropagation neural network as a look-up table for IR spectra sinnulation, The winning neuron which contains the RDF code in the upper layer of the network points to the simulated IR spectrum in the lower layer.

Several methods have been developed for establishing correlations between IR vibrational bands and substructure fragments. Counterpropagation neural networks were used to make predictions of the full spectra from RDF codes of the molecules. 

Pompe, M., Razinger, M., Novic, M. and Veber, M. (1997). Modelling of Gas Chromatographic Retention Indices Using Counterpropagation Neural Networks. Anal.Chim.Acta, 348, 215-221. 

Artificial neural networks (feed-forward neural networks, self-organizing neural networks, counterpropagation neural networks, Bayesian neural networks)... 

Novic, M. and Zupan, J., Investigation of Infrared Spectra-Structure Correlation Using Kohonen and Counterpropagation Neural Network, J. Chem. Inf. Comput. Sci., 35, 454, 1995. 

Arakawa, M., Hasegawa, K. and Funatsu, K. (2006) QSAR study of anti-HIV HEPTanalogues based on multi-objective genetic programming and counterpropagation neural network. Chemom. Intell. Lab. Syst., 83, 91-98. 

K. L. Peterson, Phys. Rev. A, 44,126 (1991). Classification of Cmll and Pul Energy Levels Using Counterpropagation Neural Networks. 

All of the algorithms described in this article have been based on an assembly of structural fragments. Recently, a new approach of 3D structure elucidation from infrared spectra by counterpropagation neural networks has been reported. Although the output structure depends on the training data set, the mechanism is quite different from the conventional method. This method is probably useful in cases where the solution space is restricted. The complementary use of both methods may open the door to new fields enhanced by structure generation. 

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