HCDT: an integrated highly confident drug–target resource. (24th November 2022)

• Record Type: Journal Article
• Authors: Chen, Jiaqi; Chen, Zhengxin; Chen, Rufei; Feng, Dehua; Li, Tianyi; Han, Huirui; Bi, Xiaoman; Wang, Zhenzhen; Li, Kongning; Li, Yongsheng; Li, Xia; Wang, Limei; Li, Jin
• Is Part Of: Database. Volume 2022(2022)
• Publication Date: 2022-11-24
• Subjects: Biology -- Databases -- Periodicals; Bioinformatics -- Periodicals; 570.285
• DOI: 10.1093/database/baac101
• Languages: English
• ISSNs: 1758-0463

Abstract

Abstract: Drug–target association plays an important role in drug discovery, drug repositioning, drug synergy prediction, etc. Currently, a lot of drug-related databases, such as DrugBank and BindingDB, have emerged. However, these databases are separate, incomplete and non-uniform with different criteria. Here, we integrated eight drug-related databases; collected, filtered and supplemented drugs, target genes and experimentally validated (highly confident) associations and built a highly confident drug–target (HCDT: http://hainmu-biobigdata.com/hcdt ) database. HCDT database includes 500 681 HCDT associations between 299 458 drugs and 5618 target genes. Compared to individual databases, HCDT database contains 1.1 to 254.2 times drugs, 1.8–5.5 times target genes and 1.4–27.7 times drug–target associations. It is normative, publicly available and easy for searching, browsing and downloading. Together with multi-omics data, it will be a good resource in analyzing the drug functional mechanism, mining drug-related biological pathways, predicting drug synergy, etc. Database URL : http://hainmu-biobigdata.com/hcdt