A new representation of molecular chirality as a fixed-length code is introduced. This code describes chiral carbon atoms using atomic properties and geometrical features independent of conformation and is able to distinguish between enantiomers. It was used as input to counterpropagation (CPG) neural networks in two different applications. In the case of a catalytic enantioselective reaction the CPG network established a correlation between the chirality codes of the catalysts and the major enantiomer obtained by the reaction. In the second application-enantioselective reduction of ketones by DIP-chloride-the series of major and minor enantiomers produced from different substrates were clustered by the CPG neural network into separate regions, one characteristic of the minor products and the other characteristic of the major products.
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