About: Predicting commercially available antiviral drugs that may act on the novel coronavirus (2019-nCoV), Wuhan, China through a drug-target interaction deep learning model

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About: Predicting commercially available antiviral drugs that may act on the novel coronavirus (2019-nCoV), Wuhan, China through a drug-target interaction deep learning model Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Predicting commercially available antiviral drugs that may act on the novel coronavirus (2019-nCoV), Wuhan, China through a drug-target interaction deep learning model
Creator	Beck, Bo Choi, Yoonjung Kang, Keunsoo Park, Sungsoo Shin, Bonggun
Source	BioRxiv; MedRxiv
abstract	The infection of a novel coronavirus found in Wuhan of China (2019-nCoV) is rapidly spreading, and the incidence rate is increasing worldwide. Due to the lack of effective treatment options for 2019-nCoV, various strategies are being tested in China, including drug repurposing. In this study, we used our pretrained deep learning-based drug-target interaction model called Molecule Transformer-Drug Target Interaction (MT-DTI) to identify commercially available drugs that could act on viral proteins of 2019-nCoV. The result showed that atazanavir, an antiretroviral medication used to treat and prevent the human immunodeficiency virus (HIV), is the best chemical compound, showing a inhibitory potency with Kd of 94.94 nM against the 2019-nCoV 3C-like proteinase, followed by efavirenz (199.17 nM), ritonavir (204.05 nM), and dolutegravir (336.91 nM). Interestingly, lopinavir, ritonavir, and darunavir are all designed to target viral proteinases. However, in our prediction, they may also bind to the replication complex components of 2019-nCoV with an inhibitory potency with Kd < 1000 nM. In addition, we also found that several antiviral agents, such as Kaletra, could be used for the treatment of 2019-nCoV, although there is no real-world evidence supporting the prediction. Overall, we suggest that the list of antiviral drugs identified by the MT-DTI model should be considered, when establishing effective treatment strategies for 2019-nCoV.
has issue date	2020-02-02(xsd:dateTime)
bibo:doi	10.1101/2020.01.31.929547
has license	biorxiv
sha1sum (hex)	5dd22f794a39c99d95a7487bacce2976a3662243
schema:url	https://doi.org/10.1101/2020.01.31.929547
resource representing a document's title	Predicting commercially available antiviral drugs that may act on the novel coronavirus (2019-nCoV), Wuhan, China through a drug-target interaction deep learning model
schema:publication	bioRxiv
resource representing a document's body	covid:5dd22f794a39c99d95a7487bacce2976a3662243#body_text
is schema:about of	named entity '2019-nCoV' named entity 'viral' named entity 'antiretroviral medication' named entity 'China' named entity 'efavirenz' named entity 'darunavir' named entity 'Kaletra' named entity 'repurpose drugs' named entity 'ritonavir' named entity 'proteinase' named entity 'drug-target' named entity '2019-nCoV' named entity 'Molecule' named entity 'RNA viruses' named entity 'atazanavir' named entity 'BindingDB' named entity '2019-nCoV' named entity '2019-nCoV' named entity 'deep learning-based' named entity '2019-nCoV' named entity 'drug-target' named entity 'lopinavir' named entity 'atazanavir' named entity 'dolutegravir' named entity 'Wuhan' named entity 'drug repurposing' named entity 'China' named entity 'infection' named entity 'drug-target' named entity 'China' named entity 'Alphacoronavirus' named entity 'Drug Target' named entity 'HIV drug' named entity 'MERS-CoV' named entity 'binding affinity' named entity 'guanosine' named entity 'epidemic' named entity '0.9' named entity 'Betacoronavirus' named entity 'lopinavir' named entity '2019-nCoV' named entity 'IC50' named entity 'Wuhan' named entity 'ganciclovir' named entity '2019-nCoV' named entity 'genus' named entity 'National Center for Biotechnology Information' named entity 'amino acid' named entity '2019-nCoV' named entity 'viral proteins' named entity 'drug target' named entity 'Deltacoronavirus' named entity 'positive-sense' named entity 'protease inhibitor' named entity 'helicase' named entity 'proteinase' named entity 'RNA-dependent RNA polymerase' named entity 'helicase' named entity 'Drug-target' named entity 'BindingDB' named entity 'efavirenz' named entity 'coronavirus' named entity 'proteinase' named entity 'Middle East respiratory syndrome coronavirus' named entity 'Gammacoronavirus' named entity 'SMILES' named entity 'darunavir' named entity 'antiviral drugs' named entity 'coronaviruses' named entity 'SARS' named entity 'DTI' named entity 'prevent the spread' named entity 'clinical study' named entity 'hydrophobic interactions' named entity 'control Table' named entity '2019-nCoV' named entity 'viral proteins' named entity 'to-5' named entity 'transformer' named entity 'antiviral drugs'

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