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

Y-H Taguchi,Turki Turki

doi:10.1371/journal.pone.0238907

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.

Highlights

Coronavirus 2019 (COVID-19) is an infectious disease that has created a pandemic worldwide [1]
We proposed an advanced unsupervised learning method working in 4D tensors for identifying numerous promising drug candidate compounds for treating COVID-19 infection
The proposed method works by applying tensor decomposition (TD)-based unsupervised feature extraction (FE) to gene expression profiles of multiple lung cancer cell lines infected by SARS-CoV-2

Summary

Introduction

Coronavirus 2019 (COVID-19) is an infectious disease that has created a pandemic worldwide [1]. Numerous studies related to identifying effective therapeutics have been reported; in slico drug discovery is a useful approach because very large numbers (up to millions) of drug candidate compounds can be screened, which is not possible using experimental approaches. As in [2,3,4,5], if gene expression profiles altered by new drug candidate compounds are coincident with those of known drug compounds, these new drug candidate compounds are regarded as promising This approach can identify promising drug candidate compounds even when they lack similarity with known drugs, as required by LBDD, and massive computational resources are not needed, as required by SBDD, it remains difficult to identify drug candidate compounds for proteins and diseases when no effective drug compounds are known. Conclusions: The drugs screened using our strategy may be effective candidates for treating patients with COVID-19

Objectives

Methods

Results

Conclusion