Automatic creation of missing groups through connectivity index for pure-component property prediction

A common frustration of using property models in general and group contribution models in particular is that the selected model may not have all the needed parameters, such as groups and/or their contributions needed to represent the molecular structure of the compound whose properties are to be estimated. Also, even if the groups are available, for some chemicals the set of groups may not be able to provide an acceptable level of prediction accuracy. One way to address these limitations with the group contribution approach is to add new groups. Addition of new groups, however, normally requires experimental data so that the new groups can be defined and their contributions estimated, which requires time and resources and is, therefore, not convenient for the model user. In this paper, a group contribution(+) approach for pure-component properties, where missing groups are created and their contributions predicted through a set of zero-order and first-order connectivity indices, is presented.