Acquisition of Knowledge on Reactions
Reaction databases provide a rich source of information on organic reactions. A self-organizing neural network is used to group individual reactions into reaction types on the basis of physicochemical description of the reaction center. This allows to detect and visualize the important characteristics - and the driving forces - of a class of reactions.
A set of chemical reactions characterized by physicochemical properties of the atoms and bonds of the reacting center is entered into a Kohonen neural network. This results in a two-dimensional landscape of organic reactions. Similar reactions are grouped into reaction types, dissimilar reactions are separated from each other.
Within the SOL project the
automatic classification of reactions performed with the program
CORA
was used to derive a classified dataset of reactions producing pyrazole derivatives.
This knowledge base is able to predict the correct regioisomer in a pyrazole
synthesis before it is practically carried out in the laboratory. This correctly
predicted regioisomer is a known COX-2 inhibitor with a high selectivity between
the COX-1 and COX-2 activity (COX-1: IC50 > 100 µM, COX-2: IC50 = 0,05 µM).
Last modified: 8. Jan. 2003,
A. Schunk