Spectrum prediction rule-based methods

By analogy to the rule-based approach, methods have been published that allow the prediction of the substitution pattern of benzene by the input of an IR spectrum. The accuracy in recognizing the proper substitution pattern is highly dependent on how good the query structure is represented by the training... 

The most common approaches to predicting spectra are based on empirical modeling, linear additivity, database retrieval, rule sets, and semiempirical methods. The availability of large spectral libraries has proved to be a valuable resource in these studies. Although ab initio theory relating to spectrum prediction is well advanced, the theoretical equations necessary for application to real-world structures are large and complex... 

Several empirical approaches for NMR spectra prediction are based on the availability of large NMR spectral databases. By using special methods for encoding substructures that correspond to particular parts of the NMR spectrum, the correlation of substructures and partial spectra can be modeled. Substructures can be encoded by using the additive model greatly developed by Pretsch [11] and Clerc [12]. The authors represented skeleton structures and substituents by individual codes and calculation rules. A more general additive model was introduced... 

This approach develops an ensemble of decision rules that are based on several classification methods that differ essentially in their mathematical formalism instead of a better prediction regularity. An independent classification of the properties of the predicted object is done by each method for each description level of each parameter group. The obtained spectrum of prediction estimates of chemical compound activity is then used in final classification procedures. 

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