Determination of the normal boiling point of chemical compounds using a quantitative structure–property relationship strategy: Application to a very large dataset

• The largest boiling point database (17,768 data) has been applied to develop and test a QSPR model.
• An accurate nonlinear model was developed to predict normal boiling point.
• Sequential search algorithm was successfully implemented to select the appropriate model parameters.
• The results show that the obtained model is the most comprehensive one available in the literature.

In this work, the quantitative structure–property relationship (QSPR) strategy is applied to predict the normal boiling point (NBP) of pure chemical compounds. In order to propose a comprehensive, reliable, and predictive model, a large dataset of 17,768 pure chemical compounds was exploited. The sequential search mathematical method has been observed to be the only viable search method capable for selection of appropriate model parameters (molecular descriptors) with regard to a data set as large as is used in this study. To develop the model, a three-layer feed forward artificial neural network has been optimized using the Levenberg–Marquardt (LM) optimization strategy. Using this dedicated strategy, satisfactory results were obtained and are quantified by the following statistical parameters: average absolute relative deviations of the predicted properties from existing literature values: 3.2%, and squared correlation coefficient: 0.94.