Accurate prediction of synergistic drug combination using a multi-source information fusion framework.

IF 4.4 1区 生物学 Q1 BIOLOGY
Shuting Jin, Huaze Long, Anqi Huang, Jianming Wang, Xuan Yu, Zhiwei Xu, Junlin Xu
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引用次数: 0

Abstract

Background: Accurately predicting synergistic drug combinations is critical for complex disease therapy. However, the vast search space of potential drug combinations poses significant challenges for identification through biological experiments alone. Nowadays, deep learning is widely applied in this field. However, most methods overlook the important role of protein-protein interaction networks formed by gene expression products and the pharmacophore information of drugs in predicting drug synergy.

Results: We propose MultiSyn, a multi-source information integration method for the accurate prediction of synergistic drug combinations. Specifically, we design a semi-supervised learning framework using an attributed graph neural network to integrate protein-protein interaction networks of gene expression products with multi-omics data, constructing initial cell line representations that incorporate multi-source information. Furthermore, we refine the initial cell line representation by adaptively integrating it with normalized gene expression profiles, enabling the extraction of cell line features that encapsulate global information. In addition, we decompose drugs into fragments containing pharmacophore information based on chemical reaction rules and construct a heterogeneous graph comprising atomic and fragment nodes. To enhance the capture of molecular structural information, we introduce a heterogeneous graph transformer to learn multi-view representations of heterogeneous molecular graphs. Extensive experiments show that MultiSyn outperforms several classical and state-of-the-art baselines in synergistic drug combination prediction tasks.

Conclusions: This study provides a powerful tool for inferring promising synergistic drug combinations. By leveraging attention mechanisms and pharmacophore information, MultiSyn identifies key substructures that are critical for synergy. Further visualization and case studies validate its effectiveness in capturing biologically meaningful features and identifying potential drug combinations.

利用多源信息融合框架准确预测协同药物组合。
背景:准确预测协同药物组合对复杂疾病的治疗至关重要。然而,潜在药物组合的巨大搜索空间对仅通过生物学实验进行鉴定提出了重大挑战。如今,深度学习在这一领域得到了广泛的应用。然而,大多数方法忽略了基因表达产物形成的蛋白-蛋白相互作用网络和药物的药效团信息在预测药物协同作用中的重要作用。结果:我们提出了一种多源信息集成方法MultiSyn,用于药物协同组合的准确预测。具体来说,我们设计了一个半监督学习框架,使用属性图神经网络将基因表达产物的蛋白质-蛋白质相互作用网络与多组学数据相结合,构建包含多源信息的初始细胞系表示。此外,我们通过自适应地将初始细胞系表示与标准化基因表达谱集成来改进初始细胞系表示,从而能够提取封装全局信息的细胞系特征。此外,我们根据化学反应规则将药物分解成包含药效团信息的片段,并构建了包含原子节点和片段节点的异构图。为了增强分子结构信息的捕获,我们引入了异构图转换器来学习异构分子图的多视图表示。大量的实验表明,MultiSyn在协同药物组合预测任务中优于几种经典和最先进的基线。结论:本研究为推测有前景的协同药物组合提供了有力的工具。通过利用注意力机制和药效团信息,MultiSyn识别出对协同作用至关重要的关键子结构。进一步的可视化和案例研究验证了其在捕获生物学上有意义的特征和识别潜在药物组合方面的有效性。
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来源期刊
BMC Biology
BMC Biology 生物-生物学
CiteScore
7.80
自引率
1.90%
发文量
260
审稿时长
3 months
期刊介绍: BMC Biology is a broad scope journal covering all areas of biology. Our content includes research articles, new methods and tools. BMC Biology also publishes reviews, Q&A, and commentaries.
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