Incorporating Multisource Knowledge To Predict Drug Synergy Based on Graph Co-regularization

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.