Entry Specification (SMILES) strings can be trained to predict the main product of chemical reactions with roughly 90% accuracy on the popular USPTO benchmark dataset. For more specialized reactivity tasks (the prediction of activation energies/reaction rates, yields, enantiomeric excess etc.), however, data tend to be scarce, and often number in the hundreds rather than in the hundreds of thousands. A straightforward application of the same type of data-hungry model architectures is generally not feasible in these cases, as training these models from scratch means that they must learn how to properly interpret a molecular structure. One potential approach to improve the data efficiency of machine learning models is introducing domain knowledge about the underlying physics/quantum chemistry into the model. Models starting from physically meaningful input representations, i.e., representations based on quantum mechanical (QM) reactivity descriptors, have been demonstrated to reach reasonable accuracy for datasets consisting of only a couple hundred or a few thousand data points, and they have been reported to be significantly more generalizable, i.e., they are significantly more accurate in out-of-sample predictions, than conventional, structure/graph-based, analogs. pensive -since they typically involve an elaborate workflow consisting of conformer generation, geometry optimization and finally a single-point density functional theory (DFT) calculation for each unique molecule -limiting their applicability in broad virtual screening campaigns. A promising strategy to circumvent this limitation is to use separate machine learning models to predict these QM reactivity descriptors on-the-fly, starting from a simple 2D (graph) or geometry-based representation. Some prominent examples of such descriptor prediction models are Chemprop, DeepMoleNet, SchNet and AIMNet . In 2020, Guan et al. proposed to directly integrate such a descriptor prediction model into a reactivity prediction model as a surrogate. In their approach, a directed message-passing neural network (D-MPNN) model was trained to predict various atom-and bondlevel QM descriptors from input molecular structures. These QM descriptors were subsequently used to augment the hidden representations of a Weisfeiler-Lehman (WL) based GNN to predict regiochemical preferences for (aromatic) substitution reactions.