Artificial neural networks in prediction of antifungal activity of a series of pyridine derivatives against Candida albicans

Quantitative structure-activity relationships (QSAR) studies of antifungal activity against Candida albicans of a large series of new pyridine derivatives were conducted with the use of artificial neural networks (ANNs). The application of ANNs has been provided with respect to the prediction of antimicrobial potency of new pyridine derivatives based on their structural descriptors generated by calculation chemistry. Antifungal activity against C. albicans has been related to a number of physicochemical and structural parameters of the pyridine derivatives investigated. The activity was expressed as logarithm of the reciprocal of the minimal inhibitory concentrations, log 1/MIC. Molecular descriptors of agents were obtained from structure fragment reference databases and by quantum-chemical calculations combined with molecular modeling. A high correlation resulted between the ANN predicted antifungal activity, log 1/MICpred, and that one from biological experiments, log 1/MICexp, for the data used in the testing set of pyridine was obtained with correlation coefficient, R, on the level of 0.9112. Crown Copyright (C) 2008 Published by Elsevier B.V. All rights reserved.