A graph neural network (GNN), consisting of a directed messagepassing neural network (D-MPNN) encoder and a feed-forward neural network (FFNN), was selected for the fast prediction of the QM descriptors. Inference for an individual molecule starts by parsing its SMILES string, after which a graph-based input representation is constructed, with atoms as nodes and bonds as edges. The D-MPNN module subsequently encodes this molecular graph, resulting in learned atom and bond features. Finally, the learned representation of the molecule is used as input to the multitask FFNN, which yields the final predictions (cf. Figure ). A more detailed description of the model architecture can be found in Section S2 of the SI.