D-MPNN 25 is one type of GNN. This work uses Chemprop, a widely used open-source D-MPNN package for the prediction of chemical properties. Chemprop utilizes a feature encoding module along with a D-MPNN to construct atomic embeddings. These embeddings are then aggregated into molecular representations, which are further processed by a feed-forward neural network (FFNN) for prediction of target properties. In the following, we briefly review how molecular encoding is achieved in Chemprop along with our modifications to incorporate additional descriptors as features. Notations are summarized in Table . In Chemprop, an input molecule M (typically input in SMILES format) is converted to a molecular graph G = M (V, E, F (n) ) where atoms are treated as vertices (V , Representation of atom i as input to the FFNN. r r r β,ij