计算药物再利用的目的是通过分析市场上已批准的药物来发现新的治疗方案。该研究提出了一种基于网络理论的药物再利用方法，提出了先前批准的化合物，可以通过改变致病蛋白的表达谱来实现。该提出的方法的新颖之处在于探索疾病致病网络和化合物特异性交互网络之间的模块相似性；因此，这种关联导致更加现实地对分子细胞响应进行系统生物学水平的建模。通过考虑网络间的所有蛋白质对，通过计算疾病网络和每个化合物特异性网络的最短路径相似度来计算疾病网络和每个化合物特异性网络之间的重叠。更高的相似性得分表示化合物的潜力显著。该方法已经在乳腺癌和肺癌方面得到了验证。当所有化合物按其归一化相似分数排序时，分别建议36种和16种药物作为乳腺癌和肺癌的新候选药物。有关候选化合物的文献调查显示，我们的一些预测已在治疗两种癌症类型的II / III期试验中得到了临床研究。总之，所提出的方法通过在网络级别的数据表示中建模生物化学细胞反应，提供了有希望的初步结果。© 2023 Wiley-VCH GmbH.

Computational drug repurposing aims to discover new treatment regimens by analyzing approved drugs on the market. This study proposes previously approved compounds that can change the expression profile of disease-causing proteins by developing a network theory-based drug repurposing approach. The novelty of the proposed approach is an exploration of module similarity between a disease-causing network and a compound-specific interaction network; thus, such an association leads to more realistic modeling of molecular cell responses at a system biology level. The overlap of the disease network and each compound-specific network is calculated based on a shortest-path similarity of networks by accounting for all protein pairs between networks. A higher similarity score indicates a significant potential of a compound. The approach was validated for breast and lung cancers. When all compounds are sorted by their normalized-similarity scores, 36 and 16 drugs are proposed as new candidates for breast and lung cancer treatment, respectively. A literature survey on candidate compounds revealed that some of our predictions have been clinically investigated in phase II/III trials for the treatment of two cancer types. As a summary, the proposed approach has provided promising initial results by modeling biochemical cell responses in a network-level data representation.© 2023 Wiley-VCH GmbH.