Abstract

Objective

To develop an end-to-end deep learning framework based on a protein–protein interaction (PPI) network to make synergistic anticancer drug combination predictions.

Materials and Methods

We propose a deep learning framework named Graph Convolutional Network for Drug Synergy (GraphSynergy). GraphSynergy adapts a spatial-based Graph Convolutional Network component to encode the high-order topological relationships in the PPI network of protein modules targeted by a pair of drugs, as well as the protein modules associated with a specific cancer cell line. The pharmacological effects of drug combinations are explicitly evaluated by their therapy and toxicity scores. An attention component is also introduced in GraphSynergy, which aims to capture the pivotal proteins that play a part in both PPI network and biomolecular interactions between drug combinations and cancer cell lines.

Results

GraphSynergy outperforms the classic and state-of-the-art models in predicting synergistic drug combinations on the 2 latest drug combination datasets. Specifically, GraphSynergy achieves accuracy values of 0.7553 (11.94% improvement compared to DeepSynergy, the latest published drug combination prediction algorithm) and 0.7557 (10.95% improvement compared to DeepSynergy) on DrugCombDB and Oncology-Screen datasets, respectively. Furthermore, the proteins allocated with high contribution weights during the training of GraphSynergy are proved to play a role in view of molecular functions and biological processes, such as transcription and transcription regulation.

Conclusion

The introduction of topological relations between drug combination and cell line within the PPI network can significantly improve the capability of synergistic drug combination identification.

中文翻译：

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GraphSynergy：一种基于网络的深度学习模型，用于抗癌药物组合预测

 抽象的

 客观的

开发基于蛋白质-蛋白质相互作用（PPI）网络的端到端深度学习框架，以进行协同抗癌药物组合预测。

 材料和方法

我们提出了一种名为药物协同图卷积网络（GraphSynergy）的深度学习框架。 GraphSynergy 采用基于空间的图卷积网络组件来编码一对药物靶向的蛋白质模块以及与特定癌细胞系相关的蛋白质模块的 PPI 网络中的高阶拓扑关系。药物组合的药理作用通过其治疗和毒性评分明确评估。 GraphSynergy 中还引入了注意力组件，旨在捕获在 PPI 网络以及药物组合与癌细胞系之间的生物分子相互作用中发挥作用的关键蛋白质。

 结果

GraphSynergy 在 2 个最新药物组合数据集上预测协同药物组合方面优于经典和最先进的模型。具体而言，GraphSynergy 在 DrugCombDB 和 Oncology-Screen 数据集上的准确率分别为 0.7553（与最新发布的药物组合预测算法 DeepSynergy 相比提高了 11.94%）和 0.7557（与 DeepSynergy 相比提高了 10.95%）。此外，在 GraphSynergy 训练过程中分配高贡献权重的蛋白质被证明在分子功能和生物过程（例如转录和转录调控）方面发挥作用。

 结论

在PPI网络内引入药物组合与细胞系之间的拓扑关系可以显着提高协同药物组合识别的能力。