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

Authorized Users Only
2012
Authors
Golubović, Jelena
Protić, Ana
Zečević, Mira
Otašević, Biljana
Mikić, Marija
Živanović, Ljiljana
Article (Published version)
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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.
Keywords:
QSRR / Artificial neural networks / Antifungal agents / Azoles / HPLC
Source:
Talanta, 2012, 100, 329-337
Publisher:
  • Elsevier Science BV, Amsterdam
Projects:
  • Synthesis, Quantitative Structure and Activity Relationship, Physico-Chemical Characterisation and Analysis of Pharmacologically Active Substances (RS-172033)

DOI: 10.1016/j.talanta.2012.07.071

ISSN: 0039-9140

PubMed: 23141345

WoS: 000313773300043

Scopus: 2-s2.0-84869079263
[ Google Scholar ]
13
13
URI
http://farfar.pharmacy.bg.ac.rs/handle/123456789/1748
Collections
  • Radovi istraživača / Researchers’ publications
Institution
Pharmacy
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. Quantitative structure retention relationships of azole antifungal agents in reversed-phase high performance liquid chromatography. Talanta. 2012;100:329-337
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.
TalantaElsevier Science BV, Amsterdam., 100, 329-337.
https://doi.org/10.1016/j.talanta.2012.07.071
Golubović Jelena, Protić Ana, Zečević Mira, Otašević Biljana, Mikić Marija, Živanović Ljiljana, "Quantitative structure retention relationships of azole antifungal agents in reversed-phase high performance liquid chromatography" 100 (2012):329-337,
https://doi.org/10.1016/j.talanta.2012.07.071 .

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