Abstract
This review article examines the role of supervised and unsupervised artificial neural networks (ANNs) in the field of quantitative structure-activity relationships (QSAR). They were found to be useful in both classical QSAR and structure-property correlation (SPC) studies where the data sets contain significant non-linear relationships. New applications of ANNs, including methods of descriptor optimisation, simultaneous prediction of multiple descriptors, the development of flexible pharmacophore models and new methods for the multidimensional reduction and display of data sets suggest that ANNs will have a useful role in the field of QSAR in the future.

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