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HCDT 2.0:一个针对经过实验验证的基因、RNA和信号通路的高可信度药物-靶点数据库。

HCDT 2.0: A Highly Confident Drug-Target Database for Experimentally Validated Genes, RNAs, and Pathways.

作者信息

Liu Xinying, Feng Dehua, Chen Jiaqi, Li Tianyi, Wang Xuefeng, Zhang Ruijie, Chen Jian, Cai Xingjun, Han Huirui, Yu Lei, Li Xia, Li Bing, Wang Limei, Li Jin

机构信息

School of Biomedical Informatics and Engineering, Kidney disease research institute at the second affiliated hospital, Hainan Engineering Research Center for Health Big Data, Hainan Medical University, Haikou, Hainan, 571199, China.

出版信息

Sci Data. 2025 Apr 25;12(1):695. doi: 10.1038/s41597-025-04981-2.

DOI:10.1038/s41597-025-04981-2
PMID:40281032
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12032214/
Abstract

Drug-target interactions constitute the fundamental basis for understanding drug action mechanisms and advancing therapeutic discovery. While existing drug-target databases have contributed valuable resources, they exhibit structural and functional fragmentation due to heterogeneous data sources and annotation standards. Building upon the high-confidence drug-gene interactions curated in HCDT 1.0, we present HCDT 2.0, a comprehensive and standardized resource that expands the scope through multiomics data integration. This update incorporates three-dimensional interactions including drug-gene, drug-RNA and drug-pathway interactions. The current version contains 1,284,353 curated interactions: 1,224,774 drug-gene pairs (678,564 drugs × 5,692 genes), 11,770 drug-RNA mappings (316 drugs × 6,430 RNAs), and 47,809 drug-pathway links (6,290 drugs × 3,143 pathways), alongside 16,317 drug-disease associations. To enhance biological interpretability, we further integrated pathway-gene and RNA-gene regulatory relationships. In addition, we integrated 38,653 negative DTIs covering 26,989 drugs and 1,575 genes. This integrative framework not only addresses critical gaps in cross-scale data representation but also establishes a robust foundation for systems pharmacology applications, including drug repurposing, adverse event prediction, and precision oncology strategies.

摘要

药物 - 靶点相互作用是理解药物作用机制和推进治疗发现的基础。虽然现有的药物 - 靶点数据库提供了宝贵的资源,但由于数据来源和注释标准的异质性,它们存在结构和功能上的碎片化问题。基于HCDT 1.0中精心整理的高可信度药物 - 基因相互作用,我们推出了HCDT 2.0,这是一个全面且标准化的资源库,通过多组学数据整合扩大了范围。此次更新纳入了三维相互作用,包括药物 - 基因、药物 - RNA和药物 - 通路相互作用。当前版本包含1,284,353条精心整理的相互作用:1,224,774对药物 - 基因(678,564种药物×5,692个基因)、11,770个药物 - RNA映射(316种药物×6,430个RNA)和47,809条药物 - 通路链接(6,290种药物×3,143条通路),以及16,317个药物 - 疾病关联。为了增强生物学解释性,我们进一步整合了通路 - 基因和RNA - 基因调控关系。此外,我们整合了38,653条负性药物 - 靶点相互作用,涉及26,989种药物和1,575个基因。这个整合框架不仅解决了跨尺度数据表示中的关键差距,还为系统药理学应用奠定了坚实基础,包括药物再利用、不良事件预测和精准肿瘤学策略。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a979/12032214/fac920087b89/41597_2025_4981_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a979/12032214/60de3080560a/41597_2025_4981_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a979/12032214/b0afa6683bb4/41597_2025_4981_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a979/12032214/fac920087b89/41597_2025_4981_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a979/12032214/60de3080560a/41597_2025_4981_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a979/12032214/b0afa6683bb4/41597_2025_4981_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a979/12032214/fac920087b89/41597_2025_4981_Fig3_HTML.jpg

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