Selected article for: "coronaviral protease dataset and deep learning"
Author: Kaifu Gao; Duc Duy Nguyen; Rui Wang; Guo-Wei Wei
Title: Machine intelligence design of 2019-nCoV drugs
  • Document date: 2020_2_4
    • ID: 1qniriu0_39_1
    Snippet: dictor (2DFP). Promising drug candidates are further evaluated by two 3D deep learning models trained with all the training sets together, including the dataset for coronaviral protease (3DALL), and the 3D deep learning multitask model trained with the dataset for coronaviral protease as a separated task (3DMT). Furthermore, we choose 15 potential anti-2019-nCoV drugs to analyze partition coefficient (logP), solubility (logS), and synthetic acces.....
    Document: dictor (2DFP). Promising drug candidates are further evaluated by two 3D deep learning models trained with all the training sets together, including the dataset for coronaviral protease (3DALL), and the 3D deep learning multitask model trained with the dataset for coronaviral protease as a separated task (3DMT). Furthermore, we choose 15 potential anti-2019-nCoV drugs to analyze partition coefficient (logP), solubility (logS), and synthetic accessibility score (SAscore) according to binding affinity ranking computed by the 3DALL model. The reasonable logP, logS, and SAscore show that our top 15 anti-2019-nCoV drug candidates are potentially effective for inhibiting 2019-nCoV. Finally, the effectiveness of some anti-HIV/AIDS drugs for treating 2019-nCoV is analyzed. Although HIV drugs Kaletra (or Aluvia) and Norvir might indeed have a moderate effect in the treatment of 2019-nCoV, the analysis of these anti-HIV/AIDS drugs together with our top 15 anti-2019-nCoV molecules shows that the new compounds generated by our GNC appear to have better druggable properties than these HIV inhibitors do.

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