Hyperparameters of LR, RF and XGBoost were tuned by minimizing the mean square error (MSE) estimated from the train set using the 10-fold cross validation method. The Hyperot library was used to search for the optimal set of hyperparameters. For the GraphConv model, we tried different numbers of units in the graph_cov layers and dense layers, learning rate, and dropout rates. The performance seemed to be more sensitive to the network size than the learning and dropout rates. We used the Python library Scikit-Learn to train LR and RF models and the XGBoost library for XGBoost models. We used the library DeepChem 31 to train the GraphConv model.