药物通过疾病治疗，预期能将细胞系统从受损状态恢复到正常状态。然而，了解药物活性或疾病发病机制下的基因调控机制仍有很长的路要走。在此，我们通过基因调控机制进行各种疾病的大规模调控分析。将转录组特征通过整合公开可用的 ChIP-seq 数据转化为转录因子的调控序列特征。与转录组特征相关的药物批准后的调控序列相关性比相关性更明显。例如，在癌症中观察到反向相关性，而在免疫系统疾病中则观察到正向相关性。在证明基于调控序列的药物发现方法的准确性和适用性后，我们预测了非小细胞肺癌的新药物并在体外验证了其抗癌活性。所提出的方法有助于理解疾病之间的关系和药物发现。 © 2022. 作者（们）。

Drugs are expected to recover the cell system away from the impaired state to normalcy through disease treatment. However, the understanding of gene regulatory machinery underlying drug activity or disease pathogenesis is far from complete. Here, we perform large-scale regulome analysis for various diseases in terms of gene regulatory machinery. Transcriptome signatures were converted into regulome signatures of transcription factors by integrating publicly available ChIP-seq data. Regulome-based correlations between diseases and their approved drugs were much clearer than the transcriptome-based correlations. For example, an inverse correlation was observed for cancers, whereas a positive correlation was observed for immune system diseases. After demonstrating the usefulness of the regulome-based drug discovery method in terms of accuracy and applicability, we predicted new drugs for nonsmall cell lung cancer and validated the anticancer activity in vitro. The proposed method is useful for understanding disease-disease relationships and drug discovery.© 2022. The Author(s).