RIGR is implemented as a featurizer in the recently released Chemprop v2. We first give a brief overview of the Chemprop architecture. The input is a SMILES string, which is converted into a molecular graph, G(V, E), using RDKit, 27 where atoms are vertices V and bonds are edges E. Atoms and bonds are separately featurized using atom-level and bondlevel features derived from their identity and topology. The architecture employs a directed message-passing neural network 7 (D-MPNN), where messages are passed between directed edges instead of between nodes as in traditional MPNNs. The learned atomic embeddings are aggregated into a molecular embedding, which can be optionally concatenated with additional molecule-level features. Finally, a feed-forward neural network (FFNN) predicts target molecular properties y using the molecular embedding as the input.