SuperTCM

SuperTCM integrates comprehensive Traditional Chinese Medicine (TCM) data to link active ingredients, biological targets, KEGG pathways, and disease associations for drug discovery and mechanistic analysis.


Key Features:

  • Extensive Database Coverage: Covers 6,516 TCM drugs (herbs) derived from 5,372 botanical species.
  • Active Ingredients and Targets: Contains 55,772 active ingredients linked to 543 biological targets.
  • Pathway Integration: Integrates 254 KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways to map compounds to biological pathways.
  • Disease Associations: Associates active ingredients and targets with 8,634 diseases.
  • Multi-source Integration: Consolidates data from published TCM databases, the official Chinese Pharmacopoeia, and Kew's Medicinal Plant Names Service.

Scientific Applications:

  • Drug Discovery: Links active ingredients to biological targets to support identification of candidate therapeutic compounds.
  • Multi-component Drug Development: Enables analysis of potential synergistic effects among multiple TCM components and recipes.
  • Pathway Analysis: Facilitates investigation of ingredient effects on biological processes via KEGG pathway mapping.
  • Disease Research: Supports targeted research by associating ingredients and targets with specific diseases.

Methodology:

Data integration was performed via systematic curation of published TCM databases, the official Chinese Pharmacopoeia, and Kew's Medicinal Plant Names Service.

Topics

Details

Cost:
Free of charge
Tool Type:
web application
Operating Systems:
Mac, Linux, Windows
Added:
3/13/2022
Last Updated:
3/13/2022

Operations

Publications

Chen Q, Springer L, Gohlke BO, Goede A, Dunkel M, Abel R, Gallo K, Preissner S, Eckert A, Seshadri L, Preissner R. SuperTCM: A biocultural database combining biological pathways and historical linguistic data of Chinese Materia Medica for drug development. Biomedicine & Pharmacotherapy. 2021;144:112315. doi:10.1016/j.biopha.2021.112315. PMID:34656056.

PMID: 34656056
Funding: - Deutsche Forschungsgemeinschaft: KFO339, TRR295