Article: Deep Learning-Based Potential Ligand Prediction Framework for COVID-19 with Drug-Target Interaction Model.
2021 , Page(s) 1–13
Abstract: 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 ... ...
| Abstract | 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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| Language | English |
| Publishing date | 2021-02-02 |
| Publishing country | United States |
| Document type | Journal Article |
| ZDB-ID | 2486574-6 |
| ISSN | 1866-9964 ; 1866-9956 |
| ISSN (online) | 1866-9964 |
| ISSN | 1866-9956 |
| DOI | 10.1007/s12559-021-09840-x |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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