Author: Bo Ram Beck; Bonggun Shin; Yoonjung Choi; Sungsoo Park; Keunsoo Kang
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 Document date: 2020_2_2
ID: cszqykpu_3
Snippet: In this study, we applied our pre-trained MT-DTI model to identify commercially available antiviral drugs that could potentially disrupt 2019-nCoV's viral components, such as proteinase, RNA-dependent RNA polymerase, and/or helicase. Since the model utilizes simplified molecular-input line-entry system (SMILES) strings and amino acid (AA) sequences, which are 1D string inputs, it is possible to quickly apply target proteins that do not have exper.....
Document: In this study, we applied our pre-trained MT-DTI model to identify commercially available antiviral drugs that could potentially disrupt 2019-nCoV's viral components, such as proteinase, RNA-dependent RNA polymerase, and/or helicase. Since the model utilizes simplified molecular-input line-entry system (SMILES) strings and amino acid (AA) sequences, which are 1D string inputs, it is possible to quickly apply target proteins that do not have experimentally confirmed 3D crystal structures, such as viral proteins of 2019-nCoV. We share a list of top commercially available antiviral drugs that could potentially hinder the multiplication cycle of 2019-nCoV with the hope that effective drugs can be developed based on these AI-proposed drug candidates and act against 2019-nCoV.
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