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Deep Learning-Based Potential Ligand Prediction Framework for COVID-19 with Drug-Target Interaction Model.

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.

SUBMITTER: Majumdar S 

PROVIDER: S-EPMC7852055 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Deep Learning-Based Potential Ligand Prediction Framework for COVID-19 with Drug-Target Interaction Model.

Majumdar Shatadru S   Nandi Soumik Kumar SK   Ghosal Shuvam S   Ghosh Bavrabi B   Mallik Writam W   Roy Nilanjana Dutta ND   Biswas Arindam A   Mukherjee Subhankar S   Pal Souvik S   Bhattacharyya Nabarun N  

Cognitive computation 20210202

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 train  ...[more]

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