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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
Carl Edwards
1Department of Computer Science University of Illinois Urbana-Champaign
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  • For correspondence: cne2@illinois.edu
Aakanksha Naik
2Allen Institute for Artificial Intelligence
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  • For correspondence: aakankshan@allenai.org
Tushar Khot
3Allen Institute for Artificial Intelligence
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  • For correspondence: tushark@allenai.org
Martin Burke
4Department of Chemistry University of Illinois Urbana-Champaign
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  • For correspondence: mdburke@illinois.edu
Heng Ji
5Department of Computer Science University of Illinois Urbana-Champaign
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  • For correspondence: hengji@illinois.edu
Tom Hope
6The Hebrew University of Jerusalem Allen Institute for Artificial Intelligence
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  • For correspondence: tomh@allenai.org
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Abstract

Predicting synergistic drug combinations can help accelerate discovery of cancer treatments, particularly therapies personalized to a patient’s specific tumor via biopsied cells. In this paper, we propose a novel setting and models for in-context drug synergy learning. We are given a small “personalized dataset” of 10-20 drug synergy relationships in the context of specific cancer cell targets. Our goal is to predict additional drug synergy relationships in that context. Inspired by recent work that pre-trains a GPT language model (LM) to “in-context learn” common function classes, we devise novel pre-training schemes that enable a GPT model to in-context learn “drug synergy functions”. Our model—which does not use any textual corpora, molecular fingerprints, protein interaction or any other domain-specific knowledge— is able to achieve competitive results. We further integrate our in-context approach with a genetic algorithm to optimize model prompts and select synergy candidates to test after conducting a patient biopsy. Finally, we explore a novel task of inverse drug design which can potentially enable the design of drugs that synergize specifically to target a given patient’s “personalized dataset”. Our findings can potentially have an important impact on precision cancer medicine, and also raise intriguing questions on non-textual pre-training for LMs.

Competing Interest Statement

The authors have declared no competing interest.

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.
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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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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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