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10.1007/s12559-021-09840-x

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33552306!7852055!33552306
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suck abstract from ncbi


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pmid33552306      Cognit+Comput 2021 ; ä (ä): 1-13
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  • Deep Learning-Based Potential Ligand Prediction Framework for COVID-19 with Drug-Target Interaction Model #MMPMID33552306
  • Majumdar S; Nandi SK; Ghosal S; Ghosh B; Mallik W; Roy ND; Biswas A; Mukherjee S; Pal S; Bhattacharyya N
  • Cognit Comput 2021[Feb]; ä (ä): 1-13 PMID33552306show ga
  • To fight against the present pandemic scenario of COVID-19 outbreak, medication with drugs and vaccines is extremely essential other than ventilation support. In this paper, we present a list of ligands which are expected to have the highest binding affinity with the S-glycoprotein of 2019-nCoV and thus can be used to make the drug for the novel coronavirus. Here, we implemented an architecture using 1D convolutional networks to predict drug-target interaction (DTI) values. The network was trained on the KIBA (Kinase Inhibitor Bioactivity) dataset. With this network, we predicted the KIBA scores (which gives a measure of binding affinity) of a list of ligands against the S-glycoprotein of 2019-nCoV. Based on these KIBA scores, we are proposing a list of ligands (33 top ligands based on best interactions) which have a high binding affinity with the S-glycoprotein of 2019-nCoV and thus can be used for the formation of drugs.
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