Combination therapy has shown an obvious efficacy on complex diseases and can greatly reduce the development of drug resistance. However, even with high-throughput screens, experimental methods are insufficient to explore novel drug combinations. In order to reduce the search space of drug combinations, there is an urgent need to develop more efficient computational methods to predict novel drug combinations. In recent decades, more and more machine learning (ML) algorithms have been applied to improve the predictive performance. The object of this study is to introduce and discuss the recent applications of ML methods and the widely used databases in drug combination prediction. In this study, we first describe the concept and controversy of synergism between drug combinations. Then, we investigate various publicly available data resources and tools for prediction tasks. Next, ML methods including classic ML and deep learning methods applied in drug combination prediction are introduced. Finally, we summarize the challenges to ML methods in prediction tasks and provide a discussion on future work.

中文翻译：

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用于药物组合预测的机器学习方法、数据库和工具

联合治疗对复杂疾病显示出明显的疗效，可大大减少耐药性的发展。然而，即使使用高通量筛选，实验方法也不足以探索新的药物组合。为了减少药物组合的搜索空间，迫切需要开发更有效的计算方法来预测新的药物组合。近几十年来，越来越多的机器学习（ML）算法被应用于提高预测性能。本研究的目的是介绍和讨论 ML 方法的最新应用以及在药物组合预测中广泛使用的数据库。在这项研究中，我们首先描述了药物组合之间协同作用的概念和争议。然后，我们调查各种公开可用的数据资源和用于预测任务的工具。接下来，介绍了包括经典ML和应用于药物组合预测的深度学习方法在内的ML方法。最后，我们总结了机器学习方法在预测任务中面临的挑战，并对未来的工作进行了讨论。