Evidence map›Paper›PMID 40281032›Full record

ArticleScientific data2025

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

Xinying Liu, Dehua Feng, Jiaqi Chen, Tianyi Li, Xuefeng Wang, Ruijie Zhang, Jian Chen, Xingjun Cai, Huirui Han, Lei Yu and 4 more

Abstract readDataset
In one paragraph

Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Xinying Liu *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.
Dehua Feng *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.
Jiaqi ChenSchool 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.
Tianyi LiSchool 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.
Xuefeng WangSchool 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.
Ruijie ZhangSchool 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.
Jian ChenSchool 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.
Xingjun CaiSchool 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.
Huirui HanSchool 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.
Lei YuSchool 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.
Xia LiSchool 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.
Bing LiSchool 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. binglijpn2003@aliyun.com.
Limei WangSchool 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. wanglm@muhn.edu.cn.
Jin LiSchool 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. lijin@muhn.edu.cn.

Funding

National Natural Science Foundation of China (National Science Foundation of China) No.32260155Natural Science Foundation of Hainan Province Nos. 821QN0894, 824RC514, 621MS041
6 · The paper itself

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.

Indexed as

Databases, GeneticRNADrug DiscoveryHumansRNA

Identifiers

PMID40281032
PMCPMC12032214

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.