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Prediction of Enantiomeric Selectivity in Chromatography by João Aires-de-Sousa; Johann Gasteiger is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is Prediction of Enantiomeric Selectivity in Chromatography about?

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 NN and accurate predictions could be obtained for independent test sets. The chirality code described here has a potential for a broad range of applicati

Who reads Prediction of Enantiomeric Selectivity in Chromatography?

Author

João Aires-de-Sousa; Johann Gasteiger

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 1093-3263)

Published

2002

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Physical Sciences)