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. 2020 Oct 30;33:106475. doi: 10.1016/j.dib.2020.106475
Subject Biochemistry
Specific subject area Chemical biological binding; Traditional Chinese Medicine; Medicinal plant; Food Biochemistry
Type of data Table
How data were acquired The data were acquired from TCMSP (Chinese Medicine System Pharmacology Database and Analysis Platform) and Swiss Target Prediction databases to sort out the potential targets of the main chemical components of the Rhizoma Polygonati. NCBI, GenCLiP3, and GeneCard were databases used to search COVID-19 related targets. Finally, the common targets were obtained by the Venny2.1.0 mapping. The tolerated doses of the compounds in human were obtained from the pharmacokinetic pkCSM database.
Data format Raw
Parameters for data collection The data were acquired from TCMSP (Chinese Medicine System Pharmacology Database and Analysis Platform) with the filtering out by the herbal medicine name "Huangjing" and bioavailability ("Oral" bioavailability) more than 30% and drug-like (DL) more than 0.18 as screening parameters for Rhizoma Polygonati. The rationale is that DL representing the chemical properties and biological properties including distribution, or toxicity related to the best clinical efficacy. OB resembles the absorption of the drug by circulation. DL≥0.18 and OB≥30% are usually used for screening conditions for active compounds in Traditional Chinese Medicine [1].
The intersection-targets of the Rhizoma Polygonati targeting COVID-19 were obtained by Venny2.1.0 based mapping.
Description of data collection Secondary Data.
Data source location Primary data sources:
TCMSP (Chinese Medicine System Pharmacology Database and Analysis Platform); NCBI, GenCLiP3, GeneCard, GEPIA, pKCSM databases.
Data accessibility With the article
Related research article Mu C, Sheng Y, Wang Q, Amin A, Li X, Xie Y. Potential compound from herbal food of rhizoma polygonati for treatment of COVID-19 analyzed by network pharmacology and molecular docking technology. J Funct Foods. 2020 Aug 14:104149. doi: 10.1016/j.jff.2020.104149. Epub ahead of print. PMID: 32837538; PMCID: PMC7427583.