Yamanaka Chikashige, Iwata Michio, Kaitoh Kazuma, Yamanishi Yoshihiro
Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Fukuoka, Japan.
Graduate School of Informatics, Nagoya University, Nagoya, Japan.
Mol Inform. 2025 Jun;44(5-6):e2444. doi: 10.1002/minf.2444.
The use of omics data, including gene expression profiles, has recently gained increasing attention in drug discovery. Omics-based drug searches and designs are often based on the correlations between chemically induced and disease-induced gene expression profiles; however, the cell specificity has not been considered. In this study, we designed a novel computational method for drug search and design using cell-specific correlations between drugs and diseases. A data completion technique allowed the characterization of cell-specific gene expression patterns in diseased cells. This proposed method was applied to search for drug candidates and generate new chemical structures for gastric cancer and atopic dermatitis. The results of drug search demonstrated that compounds with diverse chemical structures were detected and were associated with target diseases at the molecular pathway levels. The results of drug design also demonstrated that newly generated compounds were reasonable in terms of the reproducibility of registered drugs. The proposed method is expected to be useful for omics-based drug discovery.
包括基因表达谱在内的组学数据的应用，最近在药物发现中受到越来越多的关注。基于组学的药物搜索和设计通常基于化学诱导和疾病诱导的基因表达谱之间的相关性；然而，细胞特异性尚未被考虑在内。在本研究中，我们设计了一种新颖的计算方法，利用药物与疾病之间的细胞特异性相关性进行药物搜索和设计。一种数据补全技术能够表征患病细胞中的细胞特异性基因表达模式。该方法被应用于寻找胃癌和特应性皮炎的候选药物，并生成新的化学结构。药物搜索结果表明，检测到了具有不同化学结构的化合物，这些化合物在分子途径水平上与目标疾病相关。药物设计结果还表明，新生成的化合物在已注册药物的可重复性方面是合理的。该方法有望用于基于组学的药物发现。
