Details

Autor(en) / Beteiligte

• Gasteiger, Johann
• Zupan, Jure

Titel

Neural Networks in Chemistry

Ist Teil von

• Angewandte Chemie (International ed.), 1993-04, Vol.32 (4), p.503-527

Ort / Verlag

Zug: Hüthig & Wepf Verlag

Erscheinungsjahr

1993

Link zum Volltext

Quelle

Wiley Online Library

Beschreibungen/Notizen

• The capabilities of the human brain have always fascinated scientists and led them to investigate its inner workings. Over the past 50 years a number of models have been developed which have attempted to replicate the brain's various functions. At the same time the development of computers was taking a totally different direction. As a result, today's computer architectures, operating systems, and programming have very little in common with information processing as performed by the brain. Currently we are experiencing a reevaluation of the brain's abilities, and models of information processing in the brain have been translated into algorithms and made widely available. The basic building‐block of these brain models (neural networks) is an information processing unit that is a model of a neuron. An artificial neuron of this kind performs only rather simple mathematical operations; its effectiveness is derived solely from the way in which large numbers of neurons may be connected to form a network. Just as the various neural models replicate different abilities of the brain, they can be used to solve different types of problem: the classification of objects, the modeling of functional relationships, the storage and retrieval of information, and the representation of large amounts of data. This potential suggests many possibilities for the processing of chemical data, and already applications cover a wide area: spectroscopic analysis, prediction of reactions, chemical process control, and the analysis of electrostatic potentials. All these are just a small sample of the great many possibilities. The emulation of the elementary functions of the human brain is the goal of artificial neural networks. It is possible, for instance, to recognize similarities in two objects, to classify objects, to derive properties of an object from its other properties, and to transform complex relations into a simpler representation without loss of information. In chemistry neural networks have already been applied for process control, for setting up the relation between structural features and spectra, and for predicting the chemical reactivity and the secondary structure of proteins.