Prediction of Henry’s Law Constants via group-specific quantitative structure property relationships

• Regression and neural-network models for organics’ aqueous Henry’s Law Constants.
• Class-specific models found to perform better than general ones.
• Neural-network models improve general models’ accuracy; not so for class-specific.

Henry’s Law Constants (HLCs) for several hundred organic compounds in water at 25 °C were predicted by Quantitative Structure Property Relationship (QSPR) models, with the division of organic compounds into specific classes to yield more accurate models than generalised ones. Both multiple linear regression (MLR) and artificial neural network (ANN) versions of models were produced for three general cases, encompassing the entire data set; one used the six best descriptors, as determined by maximising the correlation coefficient; another used the twelve best descriptors in a similar manner, whilst the third used the same twelve descriptors as English and Carroll (2001). These achieved, respectively, root-mean square errors (RMSEs) of 0.719, 0.52 and 0.607 log(Hcc) units for the MLR version and 0.601, 0.394 and 0.431 for the test set of the ANN models, where Hcc is the ratio of the compound’s concentration in the vapour phase to that in the liquid phase. These were compared with models for six specific chemical classes: (i) alkanes, (ii) cyclic alkanes, (iii) alkenes, (iv) halogenated compounds, (v) aldehydes, ketones and esters grouped together, and (vi) monoaromatics. These group-specific models had RMSEs of 0.153, 0.141. 0.097, 0.168, 0.122 and 0.104 respectively for the MLR versions and 0.684, 0.719, 0.856, 0.784, 0.875 and 0.861 for the test set of the ANN models. It was found that the class-specific models achieved lower RMSEs than the general models, when using MLR models. The use of ANN was found to improve the predictive accuracy of the general models but failed to improve that for the class-specific models vis-à-vis MLR.