Cargando…
Deep Learning-Based Potential Ligand Prediction Framework for COVID-19 with Drug–Target Interaction Model
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...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7852055/ https://www.ncbi.nlm.nih.gov/pubmed/33552306 http://dx.doi.org/10.1007/s12559-021-09840-x |
_version_ | 1783645743929622528 |
---|---|
author | Majumdar, Shatadru Nandi, Soumik Kumar Ghosal, Shuvam Ghosh, Bavrabi Mallik, Writam Roy, Nilanjana Dutta Biswas, Arindam Mukherjee, Subhankar Pal, Souvik Bhattacharyya, Nabarun |
author_facet | Majumdar, Shatadru Nandi, Soumik Kumar Ghosal, Shuvam Ghosh, Bavrabi Mallik, Writam Roy, Nilanjana Dutta Biswas, Arindam Mukherjee, Subhankar Pal, Souvik Bhattacharyya, Nabarun |
author_sort | Majumdar, Shatadru |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7852055 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-78520552021-02-03 Deep Learning-Based Potential Ligand Prediction Framework for COVID-19 with Drug–Target Interaction Model Majumdar, Shatadru Nandi, Soumik Kumar Ghosal, Shuvam Ghosh, Bavrabi Mallik, Writam Roy, Nilanjana Dutta Biswas, Arindam Mukherjee, Subhankar Pal, Souvik Bhattacharyya, Nabarun Cognit Comput Article 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. Springer US 2021-02-02 /pmc/articles/PMC7852055/ /pubmed/33552306 http://dx.doi.org/10.1007/s12559-021-09840-x Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Majumdar, Shatadru Nandi, Soumik Kumar Ghosal, Shuvam Ghosh, Bavrabi Mallik, Writam Roy, Nilanjana Dutta Biswas, Arindam Mukherjee, Subhankar Pal, Souvik Bhattacharyya, Nabarun Deep Learning-Based Potential Ligand Prediction Framework for COVID-19 with Drug–Target Interaction Model |
title | Deep Learning-Based Potential Ligand Prediction Framework for COVID-19 with Drug–Target Interaction Model |
title_full | Deep Learning-Based Potential Ligand Prediction Framework for COVID-19 with Drug–Target Interaction Model |
title_fullStr | Deep Learning-Based Potential Ligand Prediction Framework for COVID-19 with Drug–Target Interaction Model |
title_full_unstemmed | Deep Learning-Based Potential Ligand Prediction Framework for COVID-19 with Drug–Target Interaction Model |
title_short | Deep Learning-Based Potential Ligand Prediction Framework for COVID-19 with Drug–Target Interaction Model |
title_sort | deep learning-based potential ligand prediction framework for covid-19 with drug–target interaction model |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7852055/ https://www.ncbi.nlm.nih.gov/pubmed/33552306 http://dx.doi.org/10.1007/s12559-021-09840-x |
work_keys_str_mv | AT majumdarshatadru deeplearningbasedpotentialligandpredictionframeworkforcovid19withdrugtargetinteractionmodel AT nandisoumikkumar deeplearningbasedpotentialligandpredictionframeworkforcovid19withdrugtargetinteractionmodel AT ghosalshuvam deeplearningbasedpotentialligandpredictionframeworkforcovid19withdrugtargetinteractionmodel AT ghoshbavrabi deeplearningbasedpotentialligandpredictionframeworkforcovid19withdrugtargetinteractionmodel AT mallikwritam deeplearningbasedpotentialligandpredictionframeworkforcovid19withdrugtargetinteractionmodel AT roynilanjanadutta deeplearningbasedpotentialligandpredictionframeworkforcovid19withdrugtargetinteractionmodel AT biswasarindam deeplearningbasedpotentialligandpredictionframeworkforcovid19withdrugtargetinteractionmodel AT mukherjeesubhankar deeplearningbasedpotentialligandpredictionframeworkforcovid19withdrugtargetinteractionmodel AT palsouvik deeplearningbasedpotentialligandpredictionframeworkforcovid19withdrugtargetinteractionmodel AT bhattacharyyanabarun deeplearningbasedpotentialligandpredictionframeworkforcovid19withdrugtargetinteractionmodel |