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

Bo Ram Beck, Bonggun Shin, Yoonjung Choi, Sungsoo Park, Keunsoo Kang
doi: https://doi.org/10.1101/2020.01.31.929547
Bo Ram Beck
Deargen Inc., Daejeon, Republic of Korea;
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  • For correspondence: brbr777@deargen.me
Bonggun Shin
Department of Computer Science, Emory University, Atlanta, GA, United States;
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  • For correspondence: bonggun.shin@emory.edu
Yoonjung Choi
Deargen, Inc., Daejeon, Republic of Korea;
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  • For correspondence: yoonjungc@deargen.me
Sungsoo Park
Deargen, Inc., Daejeon, Republic of Korea;
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  • For correspondence: sspark@deargen.me
Keunsoo Kang
Department of Microbiology, College of Natural Sciences, Dankook University, Cheonan, Republic of Korea
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  • For correspondence: kangk1204@gmail.com
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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 pre-trained 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.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted February 02, 2020.
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Predicting commercially available antiviral drugs that may act on the novel coronavirus (2019-nCoV), Wuhan, China through a drug-target interaction deep learning model
Bo Ram Beck, Bonggun Shin, Yoonjung Choi, Sungsoo Park, Keunsoo Kang
bioRxiv 2020.01.31.929547; doi: https://doi.org/10.1101/2020.01.31.929547
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Predicting commercially available antiviral drugs that may act on the novel coronavirus (2019-nCoV), Wuhan, China through a drug-target interaction deep learning model
Bo Ram Beck, Bonggun Shin, Yoonjung Choi, Sungsoo Park, Keunsoo Kang
bioRxiv 2020.01.31.929547; doi: https://doi.org/10.1101/2020.01.31.929547

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