Abstract

Recent work on neural networks in chemistry is reviewed and essential background to this fast-spreading method is given. Emphasis is placed on the back-propagation algorithm, because of the extensive use of this form of learning. Hopfield networks, adaptive bidirectional associative memory, and Kohonen learning are briefly described and discussed. Applications in spectroscopy (mass, infrared, ultraviolet, NMR), potentiometry, structure/activity relationships, protein structure, process control and chemical reactivity are summarized.