Additionally, the features used in ChemProp are computed with the open-source package RDKit, which trains an ensemble model containing five D-MPNNs with RDKit features. To maximize the performance, a BO-based hyperparameter optimization framework, Hyperopt, is used for tuning the model's depth, hidden size of the bond message vectors, the number of the feed-forward network layers, and the dropout probability.