Combination therapy is a promising strategy for the successful treatment of cancer. The large number of possible combinations, however, mean that it is laborious and expensive to screen for synergistic drug combinations in vitro. Nevertheless, because of the availability of high-throughput screening data and advances in computational techniques, deep learning (DL) can be a useful tool for the prediction of synergistic drug combinations. In this study, we proposed a multimodal DL framework, MMSyn, for the prediction of synergistic drug combinations. First, features embedded in the drug molecules were extracted: structure, fingerprint, and string encoding. Then, gene expression data, DNA copy number, and pathway activity were used to describe cancer cell lines. Finally, these processed features were integrated using an attention mechanism and an interaction module and then input into a multilayer perceptron to predict drug synergy. Experimental results showed that our method outperformed five state-of-the-art DL methods and three traditional machine learning models for drug combination prediction. We verified that MMSyn achieved superior performance in stratified cross-validation settings using both the drug combination and cell line data. Moreover, we performed a set of ablation experiments to illustrate the effectiveness of each component and the efficacy of our model. In addition, our visual representation and case studies further confirmed the effectiveness of our model. All results showed that MMSyn can be used as a powerful tool for the prediction of synergistic drug combinations.

中文翻译：

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MMSyn：一种新的多模态深度学习框架，用于增强协同药物组合的预测

联合疗法是成功治疗癌症的一种有前途的策略。然而，大量可能的组合意味着体外筛选协同药物组合既费力又昂贵。尽管如此，由于高通量筛选数据的可用性和计算技术的进步，深度学习（DL）可以成为预测协同药物组合的有用工具。在这项研究中，我们提出了一个多模式深度学习框架 MMSyn，用于预测协同药物组合。首先，提取药物分子中嵌入的特征：结构、指纹和字符串编码。然后，使用基因表达数据、DNA 拷贝数和通路活性来描述癌细胞系。最后，使用注意力机制和交互模块整合这些处理后的特征，然后输入到多层感知器中以预测药物协同作用。实验结果表明，我们的方法优于五种最先进的深度学习方法和三种传统的药物组合预测机器学习模型。我们使用药物组合和细胞系数据验证了 MMSyn 在分层交叉验证设置中实现了卓越的性能。此外，我们进行了一组消融实验来说明每个组件的有效性以及我们模型的功效。此外，我们的视觉表示和案例研究进一步证实了我们模型的有效性。所有结果表明，MMSyn 可以作为预测协同药物组合的有力工具。