國家衛生研究院 NHRI:Item 3990099045/12993

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Please use this identifier to cite or link to this item: http://ir.nhri.org.tw/handle/3990099045/12993

Title:	Comparative study between deep learning and QSAR classifications for TNBC inhibitors and novel GPCR agonist discovery
Authors:	Tsou, LK;Yeh, SH;Ueng, SH;Chang, CP;Song, JS;Wu, MH;Chang, HF;Chen, SR;Shih, C;Chen, CT;Ke, YY
Contributors:	Institute of Biotechnology and Pharmaceutical Research
Abstract:	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.
Date:	2020-10-08
Relation:	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
Appears in Collections:	[Chiung-Tong Chen] Periodical Articles [Chuan Shih(2014-2017)] Periodical Articles [Shau-Hua Ueng] Periodical Articles [Shiu-Hwa Yeh] Periodical Articles [Lun Kelvin Tsou] Periodical Articles [Chun-Ping Chang] Periodical Articles

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