SynerGPT: In-Context Learning for Personalized Drug Synergy Prediction
and Drug Design
- URL: http://arxiv.org/abs/2307.11694v2
- Date: Tue, 24 Oct 2023 23:51:57 GMT
- Title: SynerGPT: In-Context Learning for Personalized Drug Synergy Prediction
and Drug Design
- Authors: Carl Edwards and Aakanksha Naik and Tushar Khot and Martin Burke and
Heng Ji and Tom Hope
- Abstract summary: 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.
- Score: 64.69434941796904
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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