In order to process molecular chirality by computational methods and to obtain predictions for properties that are influenced by chirality, a fixed-length conformation-dependent chirality code is introduced. The code consists of a set of molecular descriptors representing the chirality of a 3D molecular structure. It includes information about molecular geometry and atomic properties, and can distinguish between enantiomers, even if chirality does not result from chiral centers.
The new molecular transform was applied to two datasets of chiral compounds, each of them containing pairs of enantiomers that had been separated by chiral chromatography. The elution order within each pair of isomers was predicted by means of Kohonen neural networks (NN) using the chirality codes as input. A previously described conformation-independent chirality code was also applied and the results were compared.
In both applications clustering of the two classes of enantiomers (first eluted and last eluted enantiomers) could be successfully achieved by NNs and accurate predictions could be obtained for independent test sets.
The chirality code described here has a potential for a broad range
of applications from stereoselective reactions to analytical chemistry
and to the study of biological activity of chiral compounds.