论文标题
使用图形上的深层生成模型预测药物 - 药物相互作用
Predicting Drug-Drug Interactions using Deep Generative Models on Graphs
论文作者
论文摘要
事实证明，由当代图自动编码器模型产生的药物及其靶标的潜在表示可用于预测大型网络上的许多类型的节点对相互作用，包括药物 - 毒品，药物靶标和目标目标 - 目标目标相互作用。但是，大多数现有方法对节点的潜在空间进行建模，其中节点分布是刚性和不相交的。这些局限性阻碍了方法在成对节点之间生成新链接。在本文中，我们介绍了变异图自动编码器（VGAE）在对多模式网络上的潜在节点表示建模中的有效性。我们的方法可以为多模式图的每种节点类型产生灵活的潜在空间。后来使用嵌入来预测不同边缘类型下节点对之间的链接。为了进一步增强模型的性能，我们建议一种新方法，将摩根指纹构成捕获每种药物的分子结构的新方法，并及其潜在嵌入，然后再将其前至解码阶段进行链路预测。我们提出的模型在两个多模式网络上显示了竞争性结果：（1）由药物和蛋白质节点组成的多毛牌，以及（2）由药物和细胞系节点组成的多画。我们的源代码可在https://github.com/hysonlab/drug-ingertactions上公开获得。
Latent representations of drugs and their targets produced by contemporary graph autoencoder-based models have proved useful in predicting many types of node-pair interactions on large networks, including drug-drug, drug-target, and target-target interactions. However, most existing approaches model the node's latent spaces in which node distributions are rigid and disjoint; these limitations hinder the methods from generating new links among pairs of nodes. In this paper, we present the effectiveness of variational graph autoencoders (VGAE) in modeling latent node representations on multimodal networks. Our approach can produce flexible latent spaces for each node type of the multimodal graph; the embeddings are used later for predicting links among node pairs under different edge types. To further enhance the models' performance, we suggest a new method that concatenates Morgan fingerprints, which capture the molecular structures of each drug, with their latent embeddings before preceding them to the decoding stage for link prediction. Our proposed model shows competitive results on two multimodal networks: (1) a multi-graph consisting of drug and protein nodes, and (2) a multi-graph consisting of drug and cell line nodes. Our source code is publicly available at https://github.com/HySonLab/drug-interactions.
