國家衛生研究院 NHRI:Item 3990099045/12993
English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 12145/12927 (94%)
造访人次 : 852759      在线人数 : 298
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜寻范围 查询小技巧：
  • 您可在西文检索词汇前后加上"双引号"，以获取较精准的检索结果
  • 若欲以作者姓名搜寻，建议至进阶搜寻限定作者字段，可获得较完整数据
    •  进阶搜寻
    主页 ‧ 登入 ‧ 上传 ‧ 说明 ‧ 关于NHRI ‧ 管理 到手机版


    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://ir.nhri.org.tw/handle/3990099045/12993


    题名: Comparative study between deep learning and QSAR classifications for TNBC inhibitors and novel GPCR agonist discovery
    作者: Tsou, LK;Yeh, SH;Ueng, SH;Chang, CP;Song, JS;Wu, MH;Chang, HF;Chen, SR;Shih, C;Chen, CT;Ke, YY
    贡献者: Institute of Biotechnology and Pharmaceutical Research
    摘要: Machine learning is a well-known approach for virtual screening. Recently, deep learning, a machine learning algorithm in artificial neural networks, has been applied to the advancement of precision medicine and drug discovery. In this study, we performed comparative studies between deep neural networks (DNN) and other ligand-based virtual screening (LBVS) methods to demonstrate that DNN and random forest (RF) were superior in hit prediction efficiency. By using DNN, several triple-negative breast cancer (TNBC) inhibitors were identified as potent hits from a screening of an in-house database of 165,000 compounds. In broadening the application of this method, we harnessed the predictive properties of trained model in the discovery of G protein-coupled receptor (GPCR) agonist, by which computational structure-based design of molecules could be greatly hindered by lack of structural information. Notably, a potent (~ 500 nM) mu-opioid receptor (MOR) agonist was identified as a hit from a small-size training set of 63 compounds. Our results show that DNN could be an efficient module in hit prediction and provide experimental evidence that machine learning could identify potent hits in silico from a limited training set.
    日期: 2020-10-08
    關聯: Scientific Reports. 2020 Oct 8;10:Article number 16771.
    Link to: http://dx.doi.org/10.1038/s41598-020-73681-1
    JIF/Ranking 2023: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=2045-2322&DestApp=IC2JCR
    Cited Times(WOS): https://www.webofscience.com/wos/woscc/full-record/WOS:000577473700002
    Cited Times(Scopus): https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85092155897
    显示于类别:[陳炯東] 期刊論文
    [石全(2014-2017)] 期刊論文
    [翁紹華] 期刊論文
    [葉修華] 期刊論文
    [鄒倫] 期刊論文
    [張竣評] 期刊論文

    文件中的档案:

    档案 描述 大小格式浏览次数
    SCP85092155897.pdf2103KbAdobe PDF397检视/开启


    在NHRI中所有的数据项都受到原著作权保护.

    TAIR相关文章

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回馈