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Deep learning identifies synergistic drug combinations for treating COVID-19.

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Title: Deep learning identifies synergistic drug combinations for treating COVID-19.
Authors: Wengong Jin1 wengong@csail.mit.edu, Stokes, Jonathan M.2,3, Eastman, Richard T.4, Itkin, Zina4, Zakharov, Alexey V.4, Collins, James J.2,3,5,6,7, Jaakkola, Tommi S.1, Barzilay, Regina1,5
Source: Proceedings of the National Academy of Sciences of the United States of America. 9/28/2021, Vol. 118 Issue 39, p1-7. 7p.
Subjects: COVID-19, Deep learning, Drug synergism, COVID-19 treatment, Drug abuse treatment, Drug interactions
Abstract: Effective treatments for COVID-19 are urgently needed. However, discovering single-agent therapies with activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been challenging. Combination therapies play an important role in antiviral therapies, due to their improved efficacy and reduced toxicity. Recent approaches have applied deep learning to identify synergistic drug combinations for diseases with vast preexisting datasets, but these are not applicable to new diseases with limited combination data, such as COVID-19. Given that drug synergy often occurs through inhibition of discrete biological targets, here we propose a neural network architecture that jointly learns drug-target interaction and drug-drug synergy. The model consists of two parts: a drug-target interaction module and a target-disease association module. This design enables the model to utilize drug-target interaction data and singleagent antiviral activity data, in addition to available drug-drug combination datasets, which may be small in nature. By incorporating additional biological information, our model performs significantly better in synergy prediction accuracy than previous methods with limited drug combination training data. We empirically validated our model predictions and discovered two drug combinations, remdesivir and reserpine as well as remdesivir and IQ-1S, which display strong antiviral SARS-CoV-2 synergy in vitro. Our approach, which was applied here to address the urgent threat of COVID-19, can be readily extended to other diseases for which a dearth of chemical-chemical combination data exists. [ABSTRACT FROM AUTHOR]
Copyright of Proceedings of the National Academy of Sciences of the United States of America is the property of National Academy of Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 152787130
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  Data: Deep learning identifies synergistic drug combinations for treating COVID-19.
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  Data: <searchLink fieldCode="AR" term="%22Wengong+Jin%22">Wengong Jin</searchLink><relatesTo>1</relatesTo><i> wengong@csail.mit.edu</i><br /><searchLink fieldCode="AR" term="%22Stokes%2C+Jonathan+M%2E%22">Stokes, Jonathan M.</searchLink><relatesTo>2,3</relatesTo><br /><searchLink fieldCode="AR" term="%22Eastman%2C+Richard+T%2E%22">Eastman, Richard T.</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Itkin%2C+Zina%22">Itkin, Zina</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Zakharov%2C+Alexey+V%2E%22">Zakharov, Alexey V.</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Collins%2C+James+J%2E%22">Collins, James J.</searchLink><relatesTo>2,3,5,6,7</relatesTo><br /><searchLink fieldCode="AR" term="%22Jaakkola%2C+Tommi+S%2E%22">Jaakkola, Tommi S.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Barzilay%2C+Regina%22">Barzilay, Regina</searchLink><relatesTo>1,5</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Proceedings+of+the+National+Academy+of+Sciences+of+the+United+States+of+America%22">Proceedings of the National Academy of Sciences of the United States of America</searchLink>. 9/28/2021, Vol. 118 Issue 39, p1-7. 7p.
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  Data: <searchLink fieldCode="DE" term="%22COVID-19%22">COVID-19</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Drug+synergism%22">Drug synergism</searchLink><br /><searchLink fieldCode="DE" term="%22COVID-19+treatment%22">COVID-19 treatment</searchLink><br /><searchLink fieldCode="DE" term="%22Drug+abuse+treatment%22">Drug abuse treatment</searchLink><br /><searchLink fieldCode="DE" term="%22Drug+interactions%22">Drug interactions</searchLink>
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  Label: Abstract
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  Data: Effective treatments for COVID-19 are urgently needed. However, discovering single-agent therapies with activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been challenging. Combination therapies play an important role in antiviral therapies, due to their improved efficacy and reduced toxicity. Recent approaches have applied deep learning to identify synergistic drug combinations for diseases with vast preexisting datasets, but these are not applicable to new diseases with limited combination data, such as COVID-19. Given that drug synergy often occurs through inhibition of discrete biological targets, here we propose a neural network architecture that jointly learns drug-target interaction and drug-drug synergy. The model consists of two parts: a drug-target interaction module and a target-disease association module. This design enables the model to utilize drug-target interaction data and singleagent antiviral activity data, in addition to available drug-drug combination datasets, which may be small in nature. By incorporating additional biological information, our model performs significantly better in synergy prediction accuracy than previous methods with limited drug combination training data. We empirically validated our model predictions and discovered two drug combinations, remdesivir and reserpine as well as remdesivir and IQ-1S, which display strong antiviral SARS-CoV-2 synergy in vitro. Our approach, which was applied here to address the urgent threat of COVID-19, can be readily extended to other diseases for which a dearth of chemical-chemical combination data exists. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Proceedings of the National Academy of Sciences of the United States of America is the property of National Academy of Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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        Value: 10.1073/pnas.2105070118
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      – Code: eng
        Text: English
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        PageCount: 7
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      – SubjectFull: COVID-19
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Drug synergism
        Type: general
      – SubjectFull: COVID-19 treatment
        Type: general
      – SubjectFull: Drug abuse treatment
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      – SubjectFull: Drug interactions
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    Titles:
      – TitleFull: Deep learning identifies synergistic drug combinations for treating COVID-19.
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            NameFull: Wengong Jin
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              M: 09
              Text: 9/28/2021
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              Y: 2021
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