Ionic liquids: prediction of their melting points by a recursive neural network model

A recursive neural network (RNN) was used to predict the melting points of several pyridinium-based ionic liquids (ILs). The RNN is a neural network model for processing structured data that allows for the direct handling of chemical compounds as labelled rooted ordered trees. It constitutes a direct approach to quantitative structure-property relationship (QSPR) of ILs, which avoids the use of dedicated molecular descriptors. The adopted representation of molecular structures captures significant topological aspects and chemical functionalities for each molecule in a general and flexible way. Particular emphasis was given to the representation of cyclic moieties. The model was applied to a set of 126 pyridinium bromides; it was validated by splitting the dataset into a disjoint training set (100 compounds) and test set (26 compounds). Comparison with the results obtained by other QSPR approaches on the same dataset is also presented.