A QSPR study on the liquid crystallinity of five-ring bent-core molecules using decision trees, MARS and artificial neural networks
2016
Autori
Antanasijević, JelenaAntanasijević, Davor
Pocajt, Viktor
Trišović, Nemanja
Fodor-Csorba, Katalin
Članak u časopisu (Objavljena verzija)
Metapodaci
Prikaz svih podataka o dokumentuApstrakt
Accelerating progress in the discovery of new bent-core liquid crystal (LC) materials with enhanced features relies on the understanding of structure-property relationships that underline the formation of LC phases. The aim of this study was to develop a model for the prediction of LC behaviour of five-ring bent-core systems using a QSPR approach that combines dimension reduction techniques (e.g. genetic algorithms etc.) for the selection of molecular descriptors and decision trees, multivariate adaptive regression splines (MARS) and artificial neural networks (ANN) as classification methods. A total of 27 models based on separate pools of calculated molecular descriptors (2D; 2D and 3D) and published experimental outcomes were evaluated. Overall, the results suggest that the acquired ANN LC classifiers are usable for the prediction of LC behaviour. The best of these models showed high accuracy and precision (91% and 97%). Since the best classifier is able to successfully capture trend...s in a homologous series, it can be used not only to screen new bent-core structures for potential LCs, but also for the estimation of influence of structural modifications on LC phase formation, as well as for the evaluation of LC phase stability.
Izvor:
RSC Advances, 2016, 6, 22, 18452-18464Izdavač:
- Royal Society of Chemistry
Finansiranje / projekti:
- Razvoj i primena metoda i materijala za monitoring novih zagađujućih i toksičnih organskih materija i teških metala (RS-172007)
- Proučavanje sinteze, strukture i aktivnosti organskih jedinjenja prirodnog i sintetskog porekla (RS-172013)
DOI: 10.1039/c5ra20775d
ISSN: 2046-2069
WoS: 000370717900069
Scopus: 2-s2.0-84958955480
Institucija/grupa
Tehnološko-metalurški fakultetTY - JOUR AU - Antanasijević, Jelena AU - Antanasijević, Davor AU - Pocajt, Viktor AU - Trišović, Nemanja AU - Fodor-Csorba, Katalin PY - 2016 UR - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/3392 AB - Accelerating progress in the discovery of new bent-core liquid crystal (LC) materials with enhanced features relies on the understanding of structure-property relationships that underline the formation of LC phases. The aim of this study was to develop a model for the prediction of LC behaviour of five-ring bent-core systems using a QSPR approach that combines dimension reduction techniques (e.g. genetic algorithms etc.) for the selection of molecular descriptors and decision trees, multivariate adaptive regression splines (MARS) and artificial neural networks (ANN) as classification methods. A total of 27 models based on separate pools of calculated molecular descriptors (2D; 2D and 3D) and published experimental outcomes were evaluated. Overall, the results suggest that the acquired ANN LC classifiers are usable for the prediction of LC behaviour. The best of these models showed high accuracy and precision (91% and 97%). Since the best classifier is able to successfully capture trends in a homologous series, it can be used not only to screen new bent-core structures for potential LCs, but also for the estimation of influence of structural modifications on LC phase formation, as well as for the evaluation of LC phase stability. PB - Royal Society of Chemistry T2 - RSC Advances T1 - A QSPR study on the liquid crystallinity of five-ring bent-core molecules using decision trees, MARS and artificial neural networks EP - 18464 IS - 22 SP - 18452 VL - 6 DO - 10.1039/c5ra20775d ER -
@article{ author = "Antanasijević, Jelena and Antanasijević, Davor and Pocajt, Viktor and Trišović, Nemanja and Fodor-Csorba, Katalin", year = "2016", abstract = "Accelerating progress in the discovery of new bent-core liquid crystal (LC) materials with enhanced features relies on the understanding of structure-property relationships that underline the formation of LC phases. The aim of this study was to develop a model for the prediction of LC behaviour of five-ring bent-core systems using a QSPR approach that combines dimension reduction techniques (e.g. genetic algorithms etc.) for the selection of molecular descriptors and decision trees, multivariate adaptive regression splines (MARS) and artificial neural networks (ANN) as classification methods. A total of 27 models based on separate pools of calculated molecular descriptors (2D; 2D and 3D) and published experimental outcomes were evaluated. Overall, the results suggest that the acquired ANN LC classifiers are usable for the prediction of LC behaviour. The best of these models showed high accuracy and precision (91% and 97%). Since the best classifier is able to successfully capture trends in a homologous series, it can be used not only to screen new bent-core structures for potential LCs, but also for the estimation of influence of structural modifications on LC phase formation, as well as for the evaluation of LC phase stability.", publisher = "Royal Society of Chemistry", journal = "RSC Advances", title = "A QSPR study on the liquid crystallinity of five-ring bent-core molecules using decision trees, MARS and artificial neural networks", pages = "18464-18452", number = "22", volume = "6", doi = "10.1039/c5ra20775d" }
Antanasijević, J., Antanasijević, D., Pocajt, V., Trišović, N.,& Fodor-Csorba, K.. (2016). A QSPR study on the liquid crystallinity of five-ring bent-core molecules using decision trees, MARS and artificial neural networks. in RSC Advances Royal Society of Chemistry., 6(22), 18452-18464. https://doi.org/10.1039/c5ra20775d
Antanasijević J, Antanasijević D, Pocajt V, Trišović N, Fodor-Csorba K. A QSPR study on the liquid crystallinity of five-ring bent-core molecules using decision trees, MARS and artificial neural networks. in RSC Advances. 2016;6(22):18452-18464. doi:10.1039/c5ra20775d .
Antanasijević, Jelena, Antanasijević, Davor, Pocajt, Viktor, Trišović, Nemanja, Fodor-Csorba, Katalin, "A QSPR study on the liquid crystallinity of five-ring bent-core molecules using decision trees, MARS and artificial neural networks" in RSC Advances, 6, no. 22 (2016):18452-18464, https://doi.org/10.1039/c5ra20775d . .