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

Carl Edwards, Aakanksha Naik, Tushar Khot, Martin D. Burke, Heng Ji, Tom Hope

Research Area: LMs on diverse modalities and novel applications

Keywords: in-context learning, drug synergy prediction, inverse drug design, precision medicine

TL;DR: We investigate the application of language models and in-context learning to drug synergy prediction and design.

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.

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Submission Number: 520