The Virtual Screening of the Drug Protein with a Few Crystal Structures Based on the Adaboost-SVM.

Clicks: 241
ID: 32636
2016
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.
AI Quality Assessment
Not analyzed
Readership in this journal
Popular

Ranked #28 of 44 articles by views in computational and mathematical methods in medicine

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create 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
Sign in to mint
Abstract
Using the theory of machine learning to assist the virtual screening (VS) has been an effective plan. However, the quality of the training set may reduce because of mixing with the wrong docking poses and it will affect the screening efficiencies. To solve this problem, we present a method using the ensemble learning to improve the support vector machine to process the generated protein-ligand interaction fingerprint (IFP). By combining multiple classifiers, ensemble learning is able to avoid the limitations of the single classifier's performance and obtain better generalization. According to the research of virtual screening experiment with SRC and Cathepsin K as the target, the results show that the ensemble learning method can effectively reduce the error because the sample quality is not high and improve the effect of the whole virtual screening process.
Reference Key
wang2016thecomputational Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Wang, Meng-yu;Li, Peng;Qiao, Pei-li;
Journal computational and mathematical methods in medicine
Year 2016
DOI
10.1155/2016/4809831
URL
Keywords Keywords not found

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.