Quantitative structure–retention relationships of azole antifungal agents in reversed-phase high performance liquid chromatography

Artificial neural network (ANN) is a learning system based on a computational technique which can simulate the neurological processing ability of the human brain. It was employed for building of the quantitative structure–retention relationships (QSRRs) model of antifungal agents—imidazoles or triazoles by structure. Computed molecular descriptors together with the percentage of acetonitrile in mobile phase (v/v) and buffer pH, being the most influential HPLC factors, were used as network inputs, giving the retention factor as model output. The multilayer perceptron network with a 9-5-1 topology was trained by using the back propagation algorithm. Good correlation between experimentally obtained data and ones predicted by using QSRR-ANN on previously unseen data sets indicates good predictive ability of the model.