The exploration of drug combinations presents an opportunity to amplify therapeutic effectiveness while alleviating undesirable side effects. Nevertheless, the extensive array of potential combinations poses challenges in terms of cost and time constraints for experimental screening. Thus, it is crucial to narrow down the search space. Deep learning approaches have gained widespread popularity in predicting synergistic drug combinations tailored for specific cell lines in vitro settings. In the present study, we introduce a novel method termed GTextSyn, which utilizes the integration of gene expression data and chemical structure information for the prediction of synergistic effects in drug combinations. GTextSyn employs a sentence classification model within the domain of Natural Language Processing (NLP), wherein drugs and cell lines are regarded as entities possessing biochemical relevance. Meanwhile, combinations of drug pairs and cell lines are construed as sentences with biochemical relational significance. To assess the efficacy of GTextSyn, we conduct a comparative analysis with alternative deep learning approaches using a standard benchmark dataset. The results from a five-fold cross-validation demonstrate a 49.5% reduction in Mean Square Error (MSE) achieved by GTextSyn, surpassing the performance of the next best method in the regression task. Furthermore, we conduct a comprehensive literature survey on the predicted novel drug combinations and find substantial support from prior experimental studies for many of the combinations identified by GTextSyn.

Graphical Abstract

中文翻译：

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用于预测癌症协同药物组合的深度神经网络

药物组合的探索提供了增强治疗效果同时减轻不良副作用的机会。然而，大量的潜在组合对实验筛选的成本和时间限制提出了挑战。因此，缩小搜索范围至关重要。深度学习方法在预测针对体外特定细胞系的协同药物组合方面已获得广泛流行。在本研究中，我们介绍了一种称为 GTextSyn 的新方法，该方法利用基因表达数据和化学结构信息的整合来预测药物组合中的协同效应。GTextSyn 采用自然语言处理 (NLP) 领域内的句子分类模型，其中药物和细胞系被视为具有生化相关性的实体。同时，药物对和细胞系的组合被解释为具有生化相关意义的句子。为了评估 GTextSyn 的功效，我们使用标准基准数据集与其他深度学习方法进行比较分析。五倍交叉验证的结果表明，GTextSyn 的均方误差 (MSE) 降低了 49.5%，超过了回归任务中次佳方法的性能。此外，我们对预测的新型药物组合进行了全面的文献调查，并从先前的实验研究中找到了对 GTextSyn 确定的许多组合的实质性支持。

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