HIT: linking herbal active ingredients to targets

By Hao Ye, Li Ye, Hong Kang, Duanfeng Zhang, Lin Tao, Kailin Tang, Xueping Liu, Ruixin Zhu, Qi Liu, Y. Chen and 2 more. Nucleic Acids Res., 2010

301 citations recorded by the source corpus.

The information of protein targets and small molecule has been highly valued by biomedical and pharmaceutical research. Several protein target databases are available online for FDA-approved drugs as well as the promising precursors that have largely facilitated the mechanistic study and subsequent research for drug discovery. However, those related resources regarding to herbal active ingredients, although being unusually valued as a precious resource for new drug development, is rarely found. In this article, a comprehensive and fully curated database for Herb Ingredients’ Targets (HIT, http://lifecenter.sgst.cn/hit/) has been constructed to complement above resources. Those herbal ingredients with protein target information were carefully curated. The molecular target information involves those proteins being directly/indirectly activated/inhibited, protein binders and enzymes whose substrates or products are those compounds. Those up/down regulated genes are also included under the treatment of individual ingredients. In addition, the experimental condition, observed bioactivity and various references are provided as well for user's reference. Derived from more than 3250 literatures, it currently contains 5208 entries about 1301 known protein targets (221 of them are described as direct targets) affected by 586 herbal compounds from more than 1300 reputable Chinese herbs, overlapping with 280 therapeutic targets from Therapeutic Targets Database (TTD), and 445 protein targets from DrugBank corresponding to 1488 drug agents. The database can be queried via keyword search or similarity search. Crosslinks have been made to TTD, DrugBank, KEGG, PDB, Uniprot, Pfam, NCBI, TCM-ID and other databases.

Subfields: Computational Theory and Mathematics, Pharmacology

View Paper: https://www.semanticscholar.org/paper/88cf8afb25a0b5f1329233516fd70ceccacd0b93

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Run-zhi Zhang
2. Xue Zhu
3. Kang Ning
4. Hong Bai
5. Chao Huang
6. Zi‐Yi Wang
7. Yi Zhao
8. Jiahui Zheng

In Pharmacology

1. Run-zhi Zhang
2. Xue Zhu
3. Kang Ning
4. Hong Bai
5. Zi‐Yi Wang
6. Jiahui Zheng
7. Xin Wang
8. Shao Li

Citations in the corpus

• HERB: a high-throughput experiment- and reference-guided database of traditional Chinese medicine (2020, methodology): “Prior to HERB-REF, databases like SymMap (21), HIT (15), TCMSP (23) and TCMID (22) have also curated ingredient-target relationships.”
• SymMap: an integrative database of traditional Chinese medicine enhanced by symptom mapping (2018, background): “The ingredient–target associations were obtained from the HIT and TCMSP databases, whereas the gene–disease associations were aligned and obtained from the HPO and OMIM databases.”
• TCMSP: a database of systems pharmacology for drug discovery from herbal medicines (2014, methodology): “Experimental validated drug-target pairs were retrieved from HIT database [15].”
• Network Pharmacology Databases for Traditional Chinese Medicine: Review and Assessment (2019, methodology): “For example, HIT is a comprehensive TCM database that supports many different input formats.”
• BATMAN-TCM: a Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine (2016, methodology): “Now there have been several databases which store herbal ingredients’ target information including HIT 20 , TCMID 6 and recently developed TCMSP 21 , and TCMID and TCMSP even provide herbal ingredient-target-disease network visualization, which indeed contribute to the mechanism research of TCM.”
• Network pharmacology: towards the artificial intelligence-based precision traditional Chinese medicine (2023, background): “Thus, the network relationships also include those between compounds or natural products and their relevant molecular targets mined from databases such as HIT [36],TCMGeneDIT [37],DrugCentral [38] and DrugBank [39].”
• Network Pharmacology: A New Approach for Chinese Herbal Medicine Research (2013, methodology): “[43] integrated the text-mining technology, a strict artificial audit and annotation process, and obtained the protein targets of Chinese medicine by determining the effective components based on the mass literature from PubMed.”
• TCM network pharmacology: A new trend towards combining computational, experimental and clinical approaches. (2021, background): “SymMap [51] is a TCM database focusing on the association of syndromes.”
• TCMID 2.0: a comprehensive resource for TCM (2017, background): “Comprehensive integration of various data resources, including Database@Taiwan (1), HIT (2) makes TCMID as the largest database of TCM in related field with over 49 000 prescriptions, 8159 herbs, 25 210 ingredients, 3791 diseases, 6828 drugs and 17 521 targets (3).”
• TCMID: traditional Chinese medicine integrative database for herb molecular mechanism analysis (2012, background): “Second, the information from Herb Ingredients’ Targets (HIT), which is extracted from published articles, was collected and integrated into our database.”