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A New Advanced In silico Drug Discovery Method for Novel Coronavirus (SARS-CoV-2) with Tensor Decomposition-based Unsupervised Feature Extraction
: Received: 29 April 2020 / Approved: 30 April 2020 / Online: 30 April 2020 (09:24:37 CEST)
: Received: 1 June 2020 / Approved: 3 June 2020 / Online: 3 June 2020 (05:29:09 CEST)
A peer-reviewed article of this Preprint also exists.
Journal reference: PLOS ONE 2020, 15
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.
unsupervised learning; tensor decomposition; feature selection; COVID-19; drug discovery; gene expression
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