國家衛生研究院 NHRI:Item 3990099045/12993
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    題名: 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
    貢獻者: 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
    顯示於類別:[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

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