QSPR study on melting point of carbocyclic nitroaromatic compounds by multiple linear regression and artificial neural network

•A QSPR study of melting point of carbocyclic nitroaromatic compounds was performed.•Robust and predictive models were obtained by MLR and ANN.•The applicability domain of the models was analyzed.

A quantitative structure–property relationship study was performed to correlate descriptors representing molecular structures to the melting point of carbocyclic nitroaromatic compounds. The complete set of 60 compounds was divided into a training set of 45 compounds and a test set of 15 compounds by using the DUPLEX algorithm. Multiple linear regression analysis was used to select the best subset of descriptors and to build linear models; nonlinear models were developed by means of an artificial neural network. The robustness of the obtained models was assessed by leave-one-out and leave-many-out cross-validation, Y-randomization test, and external validation through test set. The obtained models with six descriptors show good predictive power: the linear model has the average absolute relative deviation (AARD) of 5.31% and 3.98% for the training and test sets, respectively; while the nonlinear model performs better than the linear model, with the AARD of 4.42% and 3.82% for the training and test sets. In addition, the applicability domain of the models was analyzed based on the Williams plot.