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. 2020 Sep 11;15(9):e0238907.
doi: 10.1371/journal.pone.0238907. eCollection 2020.

A new advanced in silico drug discovery method for novel coronavirus (SARS-CoV-2) with tensor decomposition-based unsupervised feature extraction

Y-H Taguchi  1 , Turki Turki  2
Affiliations

A new advanced in silico drug discovery method for novel coronavirus (SARS-CoV-2) with tensor decomposition-based unsupervised feature extraction

Y-H Taguchi et al. PLoS One. .

Abstract

Background: COVID-19 is a critical pandemic that has affected human communities worldwide, and there is an urgent need to develop effective drugs. Although there are a large number of candidate drug compounds that may be useful for treating COVID-19, the evaluation of these drugs is time-consuming and costly. Thus, screening to identify potentially effective drugs prior to experimental validation is necessary.

Method: In this study, we applied the recently proposed method tensor decomposition (TD)-based unsupervised feature extraction (FE) to gene expression profiles of multiple lung cancer cell lines infected with severe acute respiratory syndrome coronavirus 2. We identified drug candidate compounds that significantly altered the expression of the 163 genes selected by TD-based unsupervised FE.

Results: Numerous drugs were successfully screened, including many known antiviral drug compounds such as C646, chelerythrine chloride, canertinib, BX-795, sorafenib, sorafenib, QL-X-138, radicicol, A-443654, CGP-60474, alvocidib, mitoxantrone, QL-XII-47, geldanamycin, fluticasone, atorvastatin, quercetin, motexafin gadolinium, trovafloxacin, doxycycline, meloxicam, gentamicin, and dibromochloromethane. The screen also identified ivermectin, which was first identified as an anti-parasite drug and recently the drug was included in clinical trials for SARS-CoV-2.

Conclusions: The drugs screened using our strategy may be effective candidates for treating patients with COVID-19.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Overall design of this study.
Fig 2
Fig 2. Singular value vectors obtained by the HOSVD algorithm.
U1:U1j, U2:U2k, U3:U1m, See Materials and methods for the definitions of j, k, and m.
See this image and copyright information in PMC

References

    1. Robson B. Computers and viral diseases. Preliminary bioinformatics studies on the design of a synthetic vaccine and a preventative peptidomimetic antagonist against the SARS-CoV-2 (2019-nCoV, COVID-19) coronavirus. Computers in Biology and Medicine. 2020; p. 103670. - PMC - PubMed
    1. Taguchi YH, Turki T. Neurological Disorder Drug Discovery from Gene Expression with Tensor Decomposition. Current Pharmaceutical Design. 2019;25(43):4589–4599. - PubMed
    1. Taguchi Y. Drug candidate identification based on gene expression of treated cells using tensor decomposition-based unsupervised feature extraction for large-scale data. BMC bioinformatics. 2019;19(13):388. - PMC - PubMed
    1. Taguchi YH. Identification of candidate drugs using tensor-decomposition-based unsupervised feature extraction in integrated analysis of gene expression between diseases and DrugMatrix datasets. Scientific reports. 2017;7(1):1–16. - PMC - PubMed
    1. Taguchi YH. Unsupervised feature extraction applied to bioinformatics: PCA and TD based approach. Switzerland: Springer International; 2020.

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