  Recent research efforts have shown the possibility to discover anticancer
drug-like molecules in food from their effect on protein-protein interaction
networks, opening a potential pathway to disease-beating diet design. We
formulate this task as a graph classification problem on which graph neural
networks (GNNs) have achieved state-of-the-art results. However, GNNs are
difficult to train on sparse low-dimensional features according to our
empirical evidence. Here, we present graph augmented features, integrating
graph structural information and raw node attributes with varying ratios, to
ease the training of networks. We further introduce a novel neural network
architecture on graphs, the Graph Attentional Autoencoder (GAA) to predict food
compounds with anticancer properties based on perturbed protein networks. We
demonstrate that the method outperforms the baseline approach and
state-of-the-art graph classification models in this task.
