  There is a perceived dichotomy between structure-based and descriptor-based
molecular representations used for predictive chemistry tasks. Here, we study
the performance, generalizability, and interpretability of the recently
proposed quantum mechanics-augmented graph neural network (ml-QM-GNN)
architecture as applied to the prediction of regioselectivity (classification)
and of activation energies (regression). In our hybrid QM-augmented model
architecture, structure-based representations are first used to predict a set
of atom- and bond-level reactivity descriptors derived from density functional
theory (DFT) calculations. These estimated reactivity descriptors are combined
with the original structure-based representation to make the final reactivity
prediction. We demonstrate that our model architecture leads to significant
improvements over structure-based GNNs in not only overall accuracy, but also
in generalization to unseen compounds. Even when provided training sets of only
a couple hundred labeled data points, the ml-QM-GNN outperforms other
state-of-the-art model architectures that have been applied to these tasks.
Further, because the predictions of our model are grounded in (but not
restricted to) QM descriptors, we are able to relate predictions to the
conceptual frameworks commonly used to gain qualitative insights into
reactivity phenomena. This effort results in a productive synergy between
theory and data science, wherein our QM-augmented models provide a data-driven
confirmation of previous qualitative analyses, and these analyses in their turn
facilitate insights into the decision-making process occurring within
ml-QM-GNNs.
