药物组合已成为癌症治疗中一种有前途的治疗方法，旨在克服耐药性并提高单一疗法的疗效。然而，传统上，识别有效的药物组合非常耗时，而且往往依赖于偶然发现。因此，迫切需要探索替代策略来支持实验研究。在这项研究中，我们提出了基于网络的预测模型来识别 11 种癌症的潜在药物组合。我们的方法包括从文献中提取 55,299 个关联，并为每种癌症类型构建人类蛋白质相互作用组。为了预测药物组合，我们测量网络内药物与药物关系的接近程度，并采用相关聚类框架来检测功能群落。最后，我们确定了 61,754 种药物组合。此外，我们分析了不同癌症类型特有的网络配置，并确定了 30 个关键基因和 21 条通路。随后通过体外测定评估这些模型的性能，结果显示出显着的一致性。这些发现对开发基于网络的药物组合设计策略做出了宝贵贡献，提出了克服耐药性和增强癌症治疗效果的潜在解决方案。版权所有 © 2024 Jiang、Wei、Lu、Li 和 Xu。

Drug combinations have emerged as a promising therapeutic approach in cancer treatment, aimed at overcoming drug resistance and improving the efficacy of monotherapy regimens. However, identifying effective drug combinations has traditionally been time-consuming and often dependent on chance discoveries. Therefore, there is an urgent need to explore alternative strategies to support experimental research. In this study, we propose network-based prediction models to identify potential drug combinations for 11 types of cancer. Our approach involves extracting 55,299 associations from literature and constructing human protein interactomes for each cancer type. To predict drug combinations, we measure the proximity of drug-drug relationships within the network and employ a correlation clustering framework to detect functional communities. Finally, we identify 61,754 drug combinations. Furthermore, we analyze the network configurations specific to different cancer types and identify 30 key genes and 21 pathways. The performance of these models is subsequently assessed through in vitro assays, which exhibit a significant level of agreement. These findings represent a valuable contribution to the development of network-based drug combination design strategies, presenting potential solutions to overcome drug resistance and enhance cancer treatment outcomes.Copyright © 2024 Jiang, Wei, Lu, Li and Xu.