Simultaneous clustering analysis with molecular docking in network pharmacology for type 2 antidiabetic compounds
Clicks: 452
ID: 33949
2017
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Steady Performance
73.1
/100
452 views
312 readers
Trending
AI Quality Assessment
Not analyzed
Readership in this journal
SteadyRanked #6 of 60 articles by views in indonesian journal of biotechnology
Most read
Least read
Bar heights use a square-root scale.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
The database of drug compounds and human proteins plays a very important role in identifying the protein target and the compound in drug discovery. Recently, a network pharmacology approach was established by updating the research paradigm from the current “one disease-one target-one drug” to a new “drug-target-disease network”. Ligand-protein interactions can be analyzed quantitatively using simultaneous clustering and molecular docking. The docking method offers the ability to quickly and cheaply predict the ligand-protein binding free energy ( DG ) in structure-based virtual screening. Meanwhile, simultaneous clustering was used to find subgroups of compounds that exhibit a high correlation with subgroups of target proteins. This study is focused on the interaction between the 306 compounds from medicinal plants (brotowali Tinospora crispa , ginger Zingiber officinale , pare Momordica charantia , sembung Blumea balsamifera , synthetic drugs (FDA-approved) and the 21 significant human proteins associated with type 2 diabetes. We found that brotowali (B018), sembung (S031), pare (P231), and ginger (J036, J033) were close to the synthetic drugs and can possibly be developed as antidiabetic drug candidates. Likewise, the proteins AKT1, WFS1, APOE, EP300, PTH, GCG, and UBC which assemble each other and which have a high association with INS can be seen as target proteins that play a role in type 2 diabetes.
| Reference Key |
rifai2017simultaneousindonesian
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Nur Azizah Komara Rifai;Farit Mochamad Afendi;I Made Sumertajaya; |
| Journal | indonesian journal of biotechnology |
| Year | 2017 |
| DOI |
10.22146/ijbiotech.27334
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.