Selected article for: "complex information and drug target interaction"
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_2
    Snippet: Unexpectedly, the world is facing the same situation as the previous outbreak due to a recent epidemic of atypical pneumonia caused by a novel coronavirus (2019-nCoV) in Wuhan, China (5, 9) . Thus, a rapid drug application strategy that can be immediately applied to the patient is necessary. Currently, the only way to address this matter is to repurpose commercially available drugs for the pathogen in so-called "drug-repurposing". However, in the.....
    Document: Unexpectedly, the world is facing the same situation as the previous outbreak due to a recent epidemic of atypical pneumonia caused by a novel coronavirus (2019-nCoV) in Wuhan, China (5, 9) . Thus, a rapid drug application strategy that can be immediately applied to the patient is necessary. Currently, the only way to address this matter is to repurpose commercially available drugs for the pathogen in so-called "drug-repurposing". However, in theory, artificial intelligence (AI)-based architectures must be taken into account in order to accurately predict drug-target interactions (DTIs). This is because of the enormous amount of complex information (e.g. hydrophobic interactions, ionic interactions, hydrogen bonding, and/or van der Waals forces) between molecules. To this end, we previously developed a deep learning-based drug-target interaction prediction model, called Molecule Transformer-Drug Target Interaction (MT-DTI) (10) .

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