About: Exploring the Ligand-Protein Networks in Traditional Chinese Medicine: Current Databases, Methods and Applications

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Exploring the Ligand-Protein Networks in Traditional Chinese Medicine: Current Databases, Methods and Applications Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Exploring the Ligand-Protein Networks in Traditional Chinese Medicine: Current Databases, Methods and Applications
Creator	Wei, Dongqing Zhao, M Shanghai, Ó Tong, Jiao Wei, Á Zhao, Mingzhu
Source	PMC
abstract	While the concept of “single component–single target” in drug discovery seems to have come to an end, “Multi-component–multi-target” is considered to be another promising way out in this field. The Traditional Chinese Medicine (TCM), which has thousands of years’ clinical application among China and other Asian countries, is the pioneer of the “Multi-component–multi-target” and network pharmacology. Hundreds of different components in a TCM prescription can cure the diseases or relieve the patients by modulating the network of potential therapeutic targets. Although there is no doubt of the efficacy, it is difficult to elucidate convincing underlying mechanism of TCM due to its complex composition and unclear pharmacology. Without thorough investigation of its potential targets and side effects, TCM is not able to generate large-scale medicinal benefits, especially in the days when scientific reductionism and quantification are dominant. The use of ligand-protein networks has been gaining significant value in the history of drug discovery while its application in TCM is still in its early stage. This article firstly surveys TCM databases for virtual screening that have been greatly expanded in size and data diversity in recent years. On that basis, different screening methods and strategies for identifying active ingredients and targets of TCM are outlined based on the amount of network information available, both on sides of ligand bioactivity and the protein structures. Furthermore, applications of successful in silico target identification attempts are discussed in details along with experiments in exploring the ligand-protein networks of TCM. Finally, it will be concluded that the prospective application of ligand-protein networks can be used not only to predict protein targets of a small molecule, but also to explore the mode of action of TCM.
has issue date	2014-06-11(xsd:dateTime)
bibo:doi	10.1007/978-94-017-9245-5_14
bibo:pmid	25387968
has license	no-cc
sha1sum (hex)	47f0a4495058390c39376534a85826d1d9379b23
schema:url	https://doi.org/10.1007/978-94-017-9245-5_14
resource representing a document's title	Exploring the Ligand-Protein Networks in Traditional Chinese Medicine: Current Databases, Methods and Applications
has PubMed Central identifier	PMC7120483
has PubMed identifier	25387968
schema:publication	Advance in Structural Bioinformatics
resource representing a document's body	covid:47f0a4495058390c39376534a85826d1d9379b23#body_text
is schema:about of	named entity 'diseases' named entity 'protein' named entity 'based' named entity 'screening' named entity 'dominant' named entity 'days' named entity 'strategies' named entity 'TCM' named entity 'Current' named entity 'networks' named entity 'identifying' named entity 'predict' named entity 'small molecule' named entity 'prescription' named entity 'history' named entity 'China' named entity 'databases' named entity 'Exploring' named entity 'TCM' named entity 'China' named entity 'TCM' named entity 'drug discovery' named entity 'ligand' named entity 'TCM' named entity 'protein' named entity 'Asian' named entity 'Traditional Chinese Medicine' named entity 'protein ligands' named entity 'SVM' named entity 'TCM' named entity 'molecular dynamics' named entity 'ligand' named entity 'kinase' named entity 'TCM' named entity 'ligand' named entity 'protein' named entity 'pharmacological' named entity 'emergentism' named entity 'virtual screening' named entity 'ligand' named entity 'western medicine' named entity 'therapeutic effects' named entity 'pharmacophores' named entity 'drug-target' named entity 'linear regression' named entity 'hydrolysis' named entity 'Alzheimer's disease' named entity 'small molecule' named entity 'network-based' named entity 'tetraarsenic' named entity 'Alzheimer's disease' named entity 'ligands' named entity 'Qing' named entity 'ligand' named entity 'TCM' named entity 'pharmacophores' named entity 'feedback loop' named entity 'China' named entity 'ligand' named entity 'synergetic' named entity 'drug-target' named entity 'virtual screening' named entity 'clinical trials' named entity 'arachidonic acid' named entity 'pharmacophore' named entity 'TCM' named entity 'drug discovery' named entity 'HIV-1 reverse transcriptase' named entity 'pharmacological' named entity 'TCM' named entity 'ligand' named entity 'Realgar' named entity 'Factor Xa' named entity 'conformation' named entity 'ligand' named entity 'bioactivity'

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2025 OpenLink Software