Quantitative structure retention relationships of azole antifungal agents in reversed-phase high performance liquid chromatography
Само за регистроване кориснике
2012
Аутори
Golubović, JelenaProtić, Ana
Zečević, Mira
Otašević, Biljana
Mikić, Marija
Živanović, Ljiljana
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
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.
Кључне речи:
QSRR / Artificial neural networks / Antifungal agents / Azoles / HPLCИзвор:
Talanta, 2012, 100, 329-337Издавач:
- Elsevier Science BV, Amsterdam
Финансирање / пројекти:
- Синтеза, квантитативни однос између структуре и дејства, физичко-хемијска карактеризација и анализа фармаколошки активних супстанци (RS-MESTD-Basic Research (BR or ON)-172033)
DOI: 10.1016/j.talanta.2012.07.071
ISSN: 0039-9140
PubMed: 23141345
WoS: 000313773300043
Scopus: 2-s2.0-84869079263
Институција/група
PharmacyTY - JOUR AU - Golubović, Jelena AU - Protić, Ana AU - Zečević, Mira AU - Otašević, Biljana AU - Mikić, Marija AU - Živanović, Ljiljana PY - 2012 UR - https://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", 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. in 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. in Talanta. 2012;100:329-337. doi: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" in Talanta, 100 (2012):329-337, https://doi.org/10.1016/j.talanta.2012.07.071 . .