T- Hop: A framework for studying the importance path information in molecular graphs for chemical property prediction
This work addresses the problem of optimizing chemical property prediction for researchers in computational chemistry, but it is incremental as it builds on prior studies to assess and predict the utility of path information.
The paper investigates the role of path information in molecular graphs for predicting chemical properties in QSAR, finding that its usefulness varies by dataset and that a simple degenerate model without path information sometimes outperforms more complex state-of-the-art methods.
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.