bibtext: @inproceedings{
  edwards2024synergpt,
  title={Syner{GPT}: In-Context Learning for Personalized Drug Synergy Prediction and Drug Design},
  author={Carl Edwards and Aakanksha Naik and Tushar Khot and Martin D. Burke and Heng Ji and Tom Hope},
  booktitle={First Conference on Language Modeling},
  year={2024},
  url={https://openreview.net/forum?id=Aaz6R4Tlwv}
  }
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 could have an important impact on precision cancer medicine, and also raise intriguing questions on non-textual pre-training for LMs.
title: SynerGPT: In-Context Learning for Personalized Drug Synergy Prediction and Drug Design
authors: Carl Edwards Aakanksha Naik Tushar Khot Martin D. Burke Heng Ji Tom Hope