随着计算机辅助的药物再利用方法的出现，它们成为高效替代昂贵且耗时的传统药物发现方法的选择。其中一些方法基于候选药物对疾病相关基因的逆转作用的假设。然而，当疾病相关基因与药物干扰基因之间的重叠有限时，这些方法就不适用了。在本研究中，我们提出了一种基于基因调控网络中的抑制效应的药物再利用方法（DRIE），用于识别癌症治疗的潜在药物。DRIE整合了基因表达谱和基因调控网络，通过使用疾病特异网络中的最短路径来计算抑制得分。在十一个数据集上的结果表明，与其他最先进的方法相比，DRIE具有优越的性能。案例研究表明我们的方法能有效地发现新的药物-疾病关联。我们的研究结果表明，排名靠前的药物候选已经得到了CTD数据库的验证。此外，它还清楚地识别了三种癌症（结直肠癌、乳腺癌和肺癌）的潜在药物，这对于在CTD中注释药物-疾病关系非常有益。本研究提出了一种新颖的药物再利用框架，对于药物发现和开发将有所帮助。© 2023 The Authors.

Numerous computational drug repurposing methods have emerged as efficient alternatives to costly and time-consuming traditional drug discovery approaches. Some of these methods are based on the assumption that the candidate drug should have a reversal effect on disease-associated genes. However, such methods are not applicable in the case that there is limited overlap between disease-related genes and drug-perturbed genes. In this study, we proposed a novel Drug Repurposing method based on the Inhibition Effect on gene regulatory network (DRIE) to identify potential drugs for cancer treatment. DRIE integrated gene expression profile and gene regulatory network to calculate inhibition score by using the shortest path in the disease-specific network. The results on eleven datasets indicated the superior performance of DRIE when compared to other state-of-the-art methods. Case studies showed that our method effectively discovered novel drug-disease associations. Our findings demonstrated that the top-ranked drug candidates had been already validated by CTD database. Additionally, it clearly identified potential agents for three cancers (colorectal, breast, and lung cancer), which was beneficial when annotating drug-disease relationships in the CTD. This study proposed a novel framework for drug repurposing, which would be helpful for drug discovery and development.© 2023 The Authors.