We use the Directed-Message Passing Neural Network (D-MPNN) model implemented in python library chemprop v1.7, which has achieved high accuracies in a variety of molecular property prediction tasks . We use the default molecular features and we tune the model hyperparameters of the chemprop library using 100 iterations of Bayesian optimization for hyperparameter search (see supporting information for more detail). The best set of parameters is chosen based on the validation error to train the final model, which is provided in the supporting information. We then explore different training approaches.