College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.
School of Information Science and Engineering, Shandong Normal University, Jinan, China.
Curr Top Med Chem. 2018;18(12):965-974. doi: 10.2174/1568026618666180330141804.
Synergistic drug combinations play an important role in the treatment of complex diseases. The identification of effective drug combination is vital to further reduce the side effects and improve therapeutic efficiency. In previous years, in vitro method has been the main route to discover synergistic drug combinations. However, many limitations of time and resource consumption lie within the in vitro method. Therefore, with the rapid development of computational models and the explosive growth of large and phenotypic data, computational methods for discovering synergistic drug combinations are an efficient and promising tool and contribute to precision medicine. It is the key of computational methods how to construct the computational model. Different computational strategies generate different performance. In this review, the recent advancements in computational methods for predicting effective drug combination are concluded from multiple aspects. First, various datasets utilized to discover synergistic drug combinations are summarized. Second, we discussed feature-based approaches and partitioned these methods into two classes including feature-based methods in terms of similarity measure, and feature-based methods in terms of machine learning. Third, we discussed network-based approaches for uncovering synergistic drug combinations. Finally, we analyzed and prospected computational methods for predicting effective drug combinations.
协同药物组合在治疗复杂疾病方面发挥着重要作用。识别有效的药物组合对于进一步降低副作用和提高治疗效果至关重要。在过去的几年中，体外方法一直是发现协同药物组合的主要途径。然而，体外方法在时间和资源消耗方面存在许多局限性。因此，随着计算模型的快速发展和大型表型数据的爆炸式增长，发现协同药物组合的计算方法是一种高效且有前途的工具，并有助于精准医学。计算方法的关键在于如何构建计算模型。不同的计算策略会产生不同的性能。在本文中，从多个方面总结了用于预测有效药物组合的计算方法的最新进展。首先，总结了用于发现协同药物组合的各种数据集。其次，我们讨论了基于特征的方法，并将这些方法分为两类，一类是基于相似性度量的基于特征的方法，另一类是基于机器学习的基于特征的方法。第三，我们讨论了用于揭示协同药物组合的网络方法。最后，我们分析和展望了用于预测有效药物组合的计算方法。
