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.
journal_name
Sci Repjournal_title
Scientific reportsauthors
Tsou LK,Yeh SH,Ueng SH,Chang CP,Song JS,Wu MH,Chang HF,Chen SR,Shih C,Chen CT,Ke YYdoi
10.1038/s41598-020-73681-1subject
Has Abstractpub_date
2020-10-08 00:00:00pages
16771issue
1issn
2045-2322pii
10.1038/s41598-020-73681-1journal_volume
10pub_type
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