Starting from the SMILES strings, two-dimensional graphs were obtained using the MolGraphConvFeaturizer from DeepChem , which represents nodes using 30 atom features and edges using 11 bond features. These are the input features for a Graph Attention Network (GAT) , also based on the DeepChem implementation. The architecture consists in two graph attention layers followed by a multilayer perceptron prediction layer . We used graph attention layers with a width of 128 dimensions and passed their aggregated outputs through a sigmoid activation function. We used dropout with 0.1 probability for the graph attention layers and 0.2 probability for the predictor layer. The output represented score predictions for each of the 38 functionals. We used an L1 loss (mean average error) to minimize the distance between the predictions and ground truth functional scores for a given molecule.