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SynerGPT: In-Context Learning for Personalized Drug Synergy Prediction and Drug Design

Carl Edwards, Aakanksha Naik, Tushar Khot, Martin Burke, Heng Ji, Tom Hope
doi: https://doi.org/10.1101/2023.07.06.547759

Article Information

doi 
https://doi.org/10.1101/2023.07.06.547759
History 
  • July 7, 2023.
Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.

Author Information

  1. Carl Edwards1,*,
  2. Aakanksha Naik2 (aakankshan{at}allenai.org),
  3. Tushar Khot3 (tushark{at}allenai.org),
  4. Martin Burke4 (mdburke{at}illinois.edu),
  5. Heng Ji5 (hengji{at}illinois.edu) and
  6. Tom Hope6 (tomh{at}allenai.org)
  1. 1Department of Computer Science University of Illinois Urbana-Champaign
  2. 2Allen Institute for Artificial Intelligence
  3. 3Allen Institute for Artificial Intelligence
  4. 4Department of Chemistry University of Illinois Urbana-Champaign
  5. 5Department of Computer Science University of Illinois Urbana-Champaign
  6. 6The Hebrew University of Jerusalem Allen Institute for Artificial Intelligence
  1. ↵*Corresponding author; email: cne2{at}illinois.edu
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Posted July 07, 2023.
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SynerGPT: In-Context Learning for Personalized Drug Synergy Prediction and Drug Design
Carl Edwards, Aakanksha Naik, Tushar Khot, Martin Burke, Heng Ji, Tom Hope
bioRxiv 2023.07.06.547759; doi: https://doi.org/10.1101/2023.07.06.547759
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