id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-317227-zb434ve3	Beck, Bo Ram	Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model	2020-03-30		.txt	text/plain	3187	174	49	title: Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model In this study, we applied our pre-trained MT-DTI model to identify commercially available antiviral drugs that could potentially disrupt SARS-CoV-2's viral components, such as proteinase, RNA-dependent RNA polymerase, and/or helicase. AutoDock Vina (version 1.1.2), which is a molecular docking and virtual screening application (17) , was used to predict binding affinities (kcal/mol) between 3C-like proteinase of SARS-CoV-2 and 3,410 FDA-approved drugs. To identify potent FDA-approved drugs that may inhibit the functions of SARS-CoV-2's core proteins, we used the MT-DTI deep learning-based model, which can accurately predict binding affinities based on chemical sequences (SMILES) and amino acid sequences (FASTA) of a target protein, without their structural information (12) . Drug-target interaction (DTI) prediction results of antiviral drugs available on markets against a novel coronavirus (SARS-CoV-2, NCBI reference sequence NC_045512.2) RNA-dependent RNA polymerase (accession YP_009725307.1).	./cache/cord-317227-zb434ve3.txt	./txt/cord-317227-zb434ve3.txt