GraphSynergy: a network-inspired deep learning model for anticancer drug combination prediction

计算机科学 药品 机器学习 交互网络 深度学习 图形 生物网络 人工智能 组分(热力学) 计算生物学 编码 生物 药理学 理论计算机科学 基因 生物化学 热力学 物理
作者
Jiannan Yang,Zhongzhi Xu,William Ka Kei Wu,Qian Chu,Qingpeng Zhang
出处
期刊:Journal of the American Medical Informatics Association [Oxford University Press]
卷期号:28 (11): 2336-2345 被引量:39
标识
DOI:10.1093/jamia/ocab162
摘要

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.
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