Classification and Prediction of Reagents' Roles by FRAU System with Self-Organizing Neural Network Model

The classification and prediction of the roles for reagents in reactions are presented. The same dimensional representation of various reagents independent of the number of atoms was achieved by selecting representative factors by the FRAU (Field-characterization for Reaction Analysis and Understanding) system. Training of a self-organizing model considering both negative and absent data was accomplished by modifying the original counter-propagation (CP) type of Kohonen neural network to treat absent data differently from negative data. The modified CP Kohonen neural network successfully classified the reagents and produced a reagent-roles correlation model that gives good answers predicting roles of the reagents.