Evidence map›Paper›PMID 37486750›Full record

ArticleJournal of chemical information and modeling2023

DCABM-TCM: A Database of Constituents Absorbed into the Blood and Metabolites of Traditional Chinese Medicine.

Xinyue Liu, Jinying Liu, Bangze Fu, Ruzhen Chen, Jianzhou Jiang, He Chen, Runa Li, Lin Xing, Liying Yuan, Xuetai Chen and 11 more

Open access · hybridAbstract read
In one paragraph

Article in Journal of chemical information and modeling, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
6.3field-weighted citation impact, top 3% of its field
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

24 citing papers in PubMed, 41 citations in OpenAlex.

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  15. Machine Learning-Enabled Drug-Induced Toxicity Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
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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

21 authors at 7 institutions in 1 country.

Xinyue LiuState Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China.
Jinying LiuCollege of Traditional Chinese Medicine, Chengde Medical University, Chengde 067000, China.
Bangze FuSchool of Biomedicine, Beijing City University, Beijing 100094, China.
Ruzhen ChenState Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China.
Jianzhou JiangState Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China.
He ChenSchool of Life Sciences, Hebei University, Baoding 071002, China.
Runa LiSchool of Biomedicine, Beijing City University, Beijing 100094, China.
Lin XingSchool of Biomedicine, Beijing City University, Beijing 100094, China.
Liying YuanSchool of Life Sciences, Hebei University, Baoding 071002, China.
Xuetai ChenSchool of Pharmaceutical Sciences, Peking University, Beijing 100191, China.
Jing ZhangSchool of Pharmaceutical Sciences, Peking University, Beijing 100191, China.
Honglei LiBeijing Cloudna Technology Company, Limited, Beijing 100029, China.
Shuzhen GuoSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China.ORCID 0000-0002-8980-9812
Feifei GuoInstitute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China.
Jiachen GuoSchool of Life Sciences, Hebei University, Baoding 071002, China.
Yuan LiuState Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China.
Yaning QiState Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China.
Biyue YuSchool of Life Sciences, Hebei University, Baoding 071002, China.
Feng XuSchool of Pharmaceutical Sciences, Peking University, Beijing 100191, China.
Dong LiState Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China.
Zhongyang LiuState Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China.
Beijing Proteome Research Center · CNBeijing City University · CNHebei University · CNPeking University · CNBeijing University of Chinese Medicine · CNChengde Medical University · CNChinese Academy of Medical Sciences & Peking Union Medical College · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Traditional Chinese medicine (TCM) not only maintains the health of Asian people but also provides a great resource of active natural products for modern drug development. Herein, we developed a Database of Constituents Absorbed into the Blood and Metabolites of TCM (DCABM-TCM), the first database systematically collecting blood constituents of TCM prescriptions and herbs, including prototypes and metabolites experimentally detected in the blood, together with the corresponding detailed detection conditions through manual literature mining. The DCABM-TCM has collected 1816 blood constituents with chemical structures of 192 prescriptions and 194 herbs and integrated their related annotations, including physicochemical, absorption, distribution, metabolism, excretion, and toxicity properties, and associated targets, pathways, and diseases. Furthermore, the DCABM-TCM supported two blood constituent-based analysis functions, the network pharmacology analysis for TCM molecular mechanism elucidation, and the target/pathway/disease-based screening of candidate blood constituents, herbs, or prescriptions for TCM-based drug discovery. The DCABM-TCM is freely accessible at http://bionet.ncpsb.org.cn/dcabm-tcm/. The DCABM-TCM will contribute to the elucidation of effective constituents and molecular mechanism of TCMs and the discovery of TCM-derived drug-like compounds that are both bioactive and bioavailable.

Indexed as

Drugs, Chinese HerbalMedicine, Chinese TraditionalDatabases, FactualHumansDrugs, Chinese Herbal

Identifiers

PMID37486750
PMCPMC10428213
OpenAlexW4385185876

What OpenQuestion 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 OpenQuestion graph.