Abstract
Artificial neural networks (ANN) based on the back-propagation algorithm (BP algorithm) were applied to a quantitative structure-activity relationship (QSAR) study for 30 azoxy compounds with antifungal activity. The ANN model could well explain the variance of the antifungal activity owing to its ability to deal with a nonlinear tendency in the data set. A modified BP algorithm proposed by the authors has provided the ANN model with a more enhanced predictive capability. Finally, a transformation of the final ANN model to a polynomial of original physico-chemical parameters was shown to be useful to elucidate the structural requirements for the antifungal activity.
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