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.

中文翻译：

---

用于抗癌 Hyperfood 预测的图形注意自动编码器

最近的研究工作表明，有可能从食物中的抗癌药物样分子对蛋白质-蛋白质相互作用网络的影响中发现它们，从而为战胜疾病的饮食设计开辟了一条潜在途径。我们将此任务制定为图分类问题，图神经网络 (GNN) 在该问题上取得了最先进的结果。然而，根据我们的经验证据，GNN 很难在稀疏的低维特征上进行训练。在这里，我们提出了图增强特征，以不同的比率集成图结构信息和原始节点属性，以简化网络的训练。我们进一步在图上引入了一种新的神经网络架构，即图注意自动编码器（GAA），以基于扰动蛋白质网络预测具有抗癌特性的食物化合物。