Prediction of the glass transition temperature of (meth)acrylic polymers containing phenyl groups by recursive neural network
ABSTRACT A recursive neural network QSPR model that can take directly molecular structures as input was applied to the prediction of the glass transition temperature of 277 poly(meth)acrylates. This model satisfactorily predicted the chemical–physical properties of high and low molecular weight acyclic compounds. However, side-chain benzene rings are present in about one half of the selected polymers. In order to render cyclic structures, the molecular representation through hierarchical structures was extended by two methods, named group and cycle breaking, respectively. The latter approach exploits standard unique molecular description systems, i.e. Unique SMILES and InChI. In all cases the prediction was very good, with 15–16 K mean absolute error and 19–21 K standard deviation. This result confirms the robustness of our method with respect to the inclusion of different structures. Moreover, the good performance of the cycle breaking representation paves the way for the investigation of data sets that contain a variety of poorly sampled cyclic structures.