Deep learning identifies synergistic drug combinations for treating COVID-19
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 effic...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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National Academy of Sciences
2021
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| Online Access: | https://hdl.handle.net/1721.1/132637 |
| _version_ | 1826203215376416768 |
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| author | Jin, Wengong Stokes, Jonathan Eastman, Richard T. Itkin, Zina Zakharov, Alexey V. Collins, James J. Jaakkola, Tommi S Barzilay, Regina |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Jin, Wengong Stokes, Jonathan Eastman, Richard T. Itkin, Zina Zakharov, Alexey V. Collins, James J. Jaakkola, Tommi S Barzilay, Regina |
| author_sort | Jin, Wengong |
| collection | MIT |
| description | 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 single-agent 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. |
| first_indexed | 2024-09-23T12:33:28Z |
| format | Article |
| id | mit-1721.1/132637 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T12:33:28Z |
| publishDate | 2021 |
| publisher | National Academy of Sciences |
| record_format | dspace |
| spelling | mit-1721.1/1326372022-09-28T08:34:43Z Deep learning identifies synergistic drug combinations for treating COVID-19 Jin, Wengong Stokes, Jonathan Eastman, Richard T. Itkin, Zina Zakharov, Alexey V. Collins, James J. Jaakkola, Tommi S Barzilay, Regina Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Biological Engineering Massachusetts Institute of Technology. Synthetic Biology Center Massachusetts Institute of Technology. Institute for Medical Engineering & Science Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 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 single-agent 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. 2021-09-24T18:41:00Z 2021-09-24T18:41:00Z 2021-09 2021-03 2021-09-24T17:18:59Z Article http://purl.org/eprint/type/JournalArticle 0027-8424 1091-6490 https://hdl.handle.net/1721.1/132637 Jin, Wengong et al. "Deep learning identifies synergistic drug combinations for treating COVID-19." Proceedings of the National Academy of Sciences 118, 39 (September 2021): e2105070118. © 2021 the Author(s) en http://dx.doi.org/10.1073/pnas.2105070118 Proceedings of the National Academy of Sciences Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf National Academy of Sciences PNAS |
| spellingShingle | Jin, Wengong Stokes, Jonathan Eastman, Richard T. Itkin, Zina Zakharov, Alexey V. Collins, James J. Jaakkola, Tommi S Barzilay, Regina Deep learning identifies synergistic drug combinations for treating COVID-19 |
| title | Deep learning identifies synergistic drug combinations for treating COVID-19 |
| title_full | Deep learning identifies synergistic drug combinations for treating COVID-19 |
| title_fullStr | Deep learning identifies synergistic drug combinations for treating COVID-19 |
| title_full_unstemmed | Deep learning identifies synergistic drug combinations for treating COVID-19 |
| title_short | Deep learning identifies synergistic drug combinations for treating COVID-19 |
| title_sort | deep learning identifies synergistic drug combinations for treating covid 19 |
| url | https://hdl.handle.net/1721.1/132637 |
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