A QSPR study on the liquid crystallinity of five-ring bent-core molecules using decision trees, MARS and artificial neural networks

Jelena Antanasijević, Davor Antanasijević, Viktor Pocajt, Nemanja Trišović, K. Fodor-Csorba

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9 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)18452-18464
Number of pages13
JournalRSC Advances
Issue number22
Publication statusPublished - 2016


ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Chemistry(all)

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