  This paper studies the usefulness of incorporating path information in
predicting chemical properties from molecular graphs, in the domain of QSAR
(Quantitative Structure-Activity Relationship). Towards this, we developed a
GNN-style model which can be toggled to operate in one of two modes: a
non-degenerate mode which incorporates path information, and a degenerate mode
which leaves out path information. Thus, by comparing the performance of the
non-degenerate mode versus the degenerate mode on relevant QSAR datasets, we
were able to directly assess the significance of path information on those
datasets. Our results corroborate previous works, by suggesting that the
usefulness of path information is datasetdependent. Unlike previous studies
however, we took the very first steps towards building a model that could
predict upfront whether or not path information would be useful for a given
dataset at hand. Moreover, we also found that, albeit its simplicity, the
degenerate mode of our model yielded rather surprising results, which
outperformed more sophisticated SOTA models in certain cases.
