School of Data Science, City University of Hong Kong, Hong Kong, S.A.R. of China.
Hong Kong Jockey Club Centre for Suicide Research and Prevention, The University of Hong Kong, Hong Kong, S.A.R. of China.
J Am Med Inform Assoc. 2021 Oct 12;28(11):2336-2345. doi: 10.1093/jamia/ocab162.
To develop an end-to-end deep learning framework based on a protein-protein interaction (PPI) network to make synergistic anticancer drug combination predictions.
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.
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.
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.
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。
我们提出了一种名为基于图卷积网络的药物协同作用（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药物靶向的蛋白质模块的 PPI 网络中的高阶拓扑关系，以及与特定癌细胞系相关的蛋白质模块。药物组合的药理效应通过其治疗和毒性评分来明确评估。GraphSynergy 中还引入了注意力组件，旨在捕获在 PPI 网络和药物组合与癌细胞系之间的生物分子相互作用中起作用的关键蛋白质。
GraphSynergy 在预测最新的两个药物组合数据集上的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（比最新发布的药物组合预测算法 DeepSynergy 提高 11.94%）和 0.7557（比 DeepSynergy 提高 10.95%）。此外，在 GraphSynergy 训练过程中分配给高贡献权重的蛋白质被证明在转录和转录调控等分子功能和生物学过程中发挥作用。
在 PPI 网络中引入药物组合与细胞系之间的拓扑关系可以显著提高协同药物组合识别的能力。
