Knowledge Discovery in Reaction Databases: Landscaping Organic Reactions by a Self-Organizing Neural Network

摘要

Chemists have always derived their knowledge about chemical reactions by inductive learning from observations on a series of individual chemical reactions. Predictions of the products of chemical reactions are made by analogy. With the availability of large reaction databases this process can be automated. In this paper a new method based on a Kohonen neural network and physicaochemical variables for describing reaction centers is developed for this purpose.