Cargando…

Prediction of Potential Commercially Available Inhibitors against SARS-CoV-2 by Multi-Task Deep Learning Model

The outbreak of COVID-19 caused millions of deaths worldwide, and the number of total infections is still rising. It is necessary to identify some potentially effective drugs that can be used to prevent the development of severe symptoms, or even death for those infected. Fortunately, many efforts h...

Descripción completa

Detalles Bibliográficos
Autores principales: Hu, Fan, Jiang, Jiaxin, Yin, Peng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9405964/
https://www.ncbi.nlm.nih.gov/pubmed/36009050
http://dx.doi.org/10.3390/biom12081156
_version_ 1784774006653583360
author Hu, Fan
Jiang, Jiaxin
Yin, Peng
author_facet Hu, Fan
Jiang, Jiaxin
Yin, Peng
author_sort Hu, Fan
collection PubMed
description The outbreak of COVID-19 caused millions of deaths worldwide, and the number of total infections is still rising. It is necessary to identify some potentially effective drugs that can be used to prevent the development of severe symptoms, or even death for those infected. Fortunately, many efforts have been made and several effective drugs have been identified. The rapidly increasing amount of data is of great help for training an effective and specific deep learning model. In this study, we propose a multi-task deep learning model for the purpose of screening commercially available and effective inhibitors against SARS-CoV-2. First, we pretrained a model on several heterogenous protein–ligand interaction datasets. The model achieved competitive results on some benchmark datasets. Next, a coronavirus-specific dataset was collected and used to fine-tune the model. Then, the fine-tuned model was used to select commercially available drugs against SARS-CoV-2 protein targets. Overall, twenty compounds were listed as potential inhibitors. We further explored the model interpretability and exhibited the predicted important binding sites. Based on this prediction, molecular docking was also performed to visualize the binding modes of the selected inhibitors.
format Online
Article
Text
id pubmed-9405964
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-94059642022-08-26 Prediction of Potential Commercially Available Inhibitors against SARS-CoV-2 by Multi-Task Deep Learning Model Hu, Fan Jiang, Jiaxin Yin, Peng Biomolecules Article The outbreak of COVID-19 caused millions of deaths worldwide, and the number of total infections is still rising. It is necessary to identify some potentially effective drugs that can be used to prevent the development of severe symptoms, or even death for those infected. Fortunately, many efforts have been made and several effective drugs have been identified. The rapidly increasing amount of data is of great help for training an effective and specific deep learning model. In this study, we propose a multi-task deep learning model for the purpose of screening commercially available and effective inhibitors against SARS-CoV-2. First, we pretrained a model on several heterogenous protein–ligand interaction datasets. The model achieved competitive results on some benchmark datasets. Next, a coronavirus-specific dataset was collected and used to fine-tune the model. Then, the fine-tuned model was used to select commercially available drugs against SARS-CoV-2 protein targets. Overall, twenty compounds were listed as potential inhibitors. We further explored the model interpretability and exhibited the predicted important binding sites. Based on this prediction, molecular docking was also performed to visualize the binding modes of the selected inhibitors. MDPI 2022-08-21 /pmc/articles/PMC9405964/ /pubmed/36009050 http://dx.doi.org/10.3390/biom12081156 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hu, Fan
Jiang, Jiaxin
Yin, Peng
Prediction of Potential Commercially Available Inhibitors against SARS-CoV-2 by Multi-Task Deep Learning Model
title Prediction of Potential Commercially Available Inhibitors against SARS-CoV-2 by Multi-Task Deep Learning Model
title_full Prediction of Potential Commercially Available Inhibitors against SARS-CoV-2 by Multi-Task Deep Learning Model
title_fullStr Prediction of Potential Commercially Available Inhibitors against SARS-CoV-2 by Multi-Task Deep Learning Model
title_full_unstemmed Prediction of Potential Commercially Available Inhibitors against SARS-CoV-2 by Multi-Task Deep Learning Model
title_short Prediction of Potential Commercially Available Inhibitors against SARS-CoV-2 by Multi-Task Deep Learning Model
title_sort prediction of potential commercially available inhibitors against sars-cov-2 by multi-task deep learning model
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9405964/
https://www.ncbi.nlm.nih.gov/pubmed/36009050
http://dx.doi.org/10.3390/biom12081156
work_keys_str_mv AT hufan predictionofpotentialcommerciallyavailableinhibitorsagainstsarscov2bymultitaskdeeplearningmodel
AT jiangjiaxin predictionofpotentialcommerciallyavailableinhibitorsagainstsarscov2bymultitaskdeeplearningmodel
AT yinpeng predictionofpotentialcommerciallyavailableinhibitorsagainstsarscov2bymultitaskdeeplearningmodel