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.
TY - JOUR
AU - Golubović, Jelena
AU - Protić, Ana
AU - Zečević, Mira
AU - Otašević, Biljana
AU - Mikić, Marija
AU - Živanović, Ljiljana
PY - 2012
UR - http://farfar.pharmacy.bg.ac.rs/handle/123456789/1748
AB - 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.
PB - Elsevier Science BV, Amsterdam
T2 - Talanta
T1 - Quantitative structure retention relationships of azole antifungal agents in reversed-phase high performance liquid chromatography
VL - 100
SP - 329
EP - 337
DO - 10.1016/j.talanta.2012.07.071
ER -
@article{
author = "Golubović, Jelena and Protić, Ana and Zečević, Mira and Otašević, Biljana and Mikić, Marija and Živanović, Ljiljana",
year = "2012",
url = "http://farfar.pharmacy.bg.ac.rs/handle/123456789/1748",
abstract = "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.",
publisher = "Elsevier Science BV, Amsterdam",
journal = "Talanta",
title = "Quantitative structure retention relationships of azole antifungal agents in reversed-phase high performance liquid chromatography",
volume = "100",
pages = "329-337",
doi = "10.1016/j.talanta.2012.07.071"
}
Golubović, J., Protić, A., Zečević, M., Otašević, B., Mikić, M.,& Živanović, L. (2012). Quantitative structure retention relationships of azole antifungal agents in reversed-phase high performance liquid chromatography.
Talanta
Elsevier Science BV, Amsterdam., 100, 329-337.
https://doi.org/10.1016/j.talanta.2012.07.071
Golubović J, Protić A, Zečević M, Otašević B, Mikić M, Živanović L. Quantitative structure retention relationships of azole antifungal agents in reversed-phase high performance liquid chromatography. Talanta. 2012;100:329-337
