Molecules, and molecules within reactions, are represented similarly as heterographs with atom, bond, and global feature nodes (Figure ). Heterographs, as opposed to homographs with bonds as edges, allow for separate relationships between each different edge type and enable the addition of a separate global node type to store important molecular-level information. Graphs (G = (B, A, g)) consist of B as bond information vectors, A is atom-level information, and g is the molecular-level feature vector. This followed prior work that also featurized molecules as complex knowledge graphs . Notably, we also intake user information on molecular charge and spin information, and transform it into one-hot encoded vectors in the global feature vector g. Features from the original graph encoding are transformed via iterative message-passing steps to yield an updated molecular graph G ′ = (B ′ , A ′ , g ′ ) with updated node features B ′ , A ′ , g ′ . These features are embedded into a fixed-size vector prior to a dense, feedforward network for property prediction similar to other molecular property graph neural networks .