Counter-propagation artificial neural networks as a tool for prediction of pKBH+ for series of amides

In this work counter-propagation artificial neural networks (CPANN) were used as a tool for development of interpretable quantitative structure–property relationship (QSPR) models for prediction of pKBH+ values of a series of amides. The methodology used here is based on our recently developed algorithm for automatic adjustment of the relative importance of the input variables for training of the CPANN. Using this novel algorithm we were able to develop several simple QSPR models. the best models, discussed in details in the article, has only three interpretable descriptors: number of halogen atoms in the structure, the energy of the lowest unoccupied molecular orbital (LUMO) which reflects the electronic properties of the molecules and the average molecular weight. The final analysis of this model shows that the most responsible for modeling of the pKBH+ values is the number of the present halogen atoms in the structures. Similar relative importance has LUMO. This descriptor helps in groping of the similar substances in different part of the CPANN. While the average molecular weight, with nearly seven times smaller relative importance compared to previous two descriptors, is related to the influence of the presence of, in most of the cases, more than one halogen atom in the structures on pKBH+. Finally, the developed models have excellent generalization performances which were checked using independent test set.