Drug combinations may reduce toxicity and increase therapeutic efficacy, offering a promising strategy to conquer multiple complex diseases. However, due to large-scale combinatorial space, it remains challenging to identify effective combinations. Although many computational methods have focused on predicting drug synergy to reduce combinatorial space, they fail to effectively consider multiple sources of important knowledge. Thus, it is necessary to propose a computational method that can exploit useful information to predict drug synergy. Here, we developed a computational method to predict drug synergy based on graph co-regularization, named DSGCR. By incorporating drug-target network patterns, pharmacological patterns, and prior knowledge of drug combinations, DSGCR performs predictions of synergistic drug combinations. Compared to several existing methods, DSGCR achieves superior performance in predicting drug synergy in terms of various metrics via cross-validation. Additionally, we analyzed the importance of various sources of drug knowledge concerning three DSGCR's scenarios. Finally, the potential of DSGCR to score drug synergy was confirmed by three predicted synergistic drug combinations.

中文翻译：

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整合多源知识以基于图协同正则化预测药物协同作用。

药物组合可降低毒性并提高治疗效果，为克服多种复杂疾病提供了一种有前途的策略。然而，由于大规模的组合空间，确定有效的组合仍然具有挑战性。尽管许多计算方法集中于预测药物协同作用以减少组合空间，但它们未能有效地考虑重要知识的多种来源。因此，有必要提出一种可以利用有用信息来预测药物协同作用的计算方法。在这里，我们开发了一种基于图协正则化的预测药物协同作用的计算方法，称为DSGCR。通过合并药物目标网络模式，药理模式和药物组合的先验知识，DSGCR可以进行协同药物组合的预测。与几种现有方法相比，DSGCR在通过交叉验证的各种指标上预测药物协同作用方面具有出色的性能。此外，我们分析了有关三种DSGCR方案的各种药物知识来源的重要性。最后，通过三种预测的协同药物组合证实了DSGCR可能对药物协同作用进行评分。