QSPR prediction of polymers’ solubility parameters by radial basis functional link net


Journal of Computational Methods in Sciences and Engineering, vol.20, pp.1341-1356, 2020 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 20
  • Publication Date: 2020
  • Doi Number: 10.3233/jcm-200033
  • Journal Name: Journal of Computational Methods in Sciences and Engineering
  • Journal Indexes: Emerging Sources Citation Index, Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Page Numbers: pp.1341-1356
  • Keywords: Polymer solubility, neural networks, radial basis functional link net, QSPR, cuckoo search, ARTIFICIAL NEURAL-NETWORK, CONNECTIONIST METHODS, CUCKOO SEARCH, ANN, MODEL, APPLICABILITY, NANOFLUID, VISCOSITY, SELECTION


This research aims to introduce a novel radial basis functional link net (RBFLN)-based QSPR (quantitative structure-property relationship) model to predict the solubility parameters of the polymers with the structure - ((CH2)-H-1 - - (CRR4)-R-2-R-3) - and provides its comparison with the multi-layer feed forward network (MLFFN)-based QSPR model, as well as previous genetic programming (GP) and multiple linear regression (MLR)-based QSPR models in the literature. During the implementation of the RBFLN and MLFFN-based QSPR models, the networks which are associated with the minimum weighted average AIC (Akaike's information criterion) and BIC (Bayesian information criterion) scores are trained by using a hybrid scheme combining the cuckoo search and Levenberg-Marquardt algorithm. Our results show that the RBFLN-based QSPR model outperforms the other ones in terms of the external validation metrics. The study also reveals that it may have a promising potential to study the relationship between various measurement/experimental data or processing elements in a hybrid way of artificial intelligence modelling.