title: Artificial neural networks for guest chirality classification through supramolecular interactions.
abstract: A novel strategy for classification of guest chirality based on the combination of artificial neural networks and anion-receptor chemistry is reported. The receptor reported herein forms supramolecular complexes with a variety of biologically important carboxylates, in which the chemical shift changes during addition of anions result in complex guest-stereochemistry-dependent patterns as followed by (1) H NMR spectroscopy. The neural network had learnt these patterns from a training set of 12 anions, and successfully identified the "unknown" chirality of 14 guests present in the test set. Additionally, principal component analysis could discriminate most of the guests studied (26) and allowed for identification of the receptor protons, which are responsible for information transfer of guest chirality.
authors.affiliation: Institute of Organic Chemistry, Polish Academy of Sciences, Kasprzaka 44/52, 01-224, Warsaw (Poland).
authors.forename: Jarosław M
authors.initials: JM
authors.lastname: Granda
authors.forename: Janusz
authors.initials: J
authors.lastname: Jurczak
keywords: anions; chirality; receptors; structure-activity relationships; supramolecular chemistry