Preprint Article Version 1 This version is not peer-reviewed

A New Advanced In Silico Drug Discovery Method for Novel Coronavirus (SARS-CoV-2) with Tensor Decomposition-Based Unsupervised Feature Extraction

Version 1 : Received: 29 April 2020 / Approved: 30 April 2020 / Online: 30 April 2020 (09:24:37 CEST)

How to cite: Taguchi, Y.; Turki, T. A New Advanced In Silico Drug Discovery Method for Novel Coronavirus (SARS-CoV-2) with Tensor Decomposition-Based Unsupervised Feature Extraction. Preprints 2020, 2020040524 (doi: 10.20944/preprints202004.0524.v1). Taguchi, Y.; Turki, T. A New Advanced In Silico Drug Discovery Method for Novel Coronavirus (SARS-CoV-2) with Tensor Decomposition-Based Unsupervised Feature Extraction. Preprints 2020, 2020040524 (doi: 10.20944/preprints202004.0524.v1).

Abstract

Background: COVID-19 is a critical pandemic that has affected human communities worldwide. Although it is urgent to rapidly develop effective drugs, large number of candidate drug compounds may be useful for treating COVID-19, and 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. Conclusions: The drugs screened using our strategy may be effective candidates for treating patients with COVID-19.

Subject Areas

unsupervised learning; tensor decomposition; feature selection; COVID-19; drug discovery; gene expression

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