PT  - JOURNAL ARTICLE
AU  - Edwards, Carl
AU  - Naik, Aakanksha
AU  - Khot, Tushar
AU  - Burke, Martin
AU  - Ji, Heng
AU  - Hope, Tom
TI  - SynerGPT: In-Context Learning for Personalized Drug Synergy Prediction and Drug Design
AID  - 10.1101/2023.07.06.547759
DP  - 2023 Jan 01
TA  - bioRxiv
PG  - 2023.07.06.547759
4099  - http://biorxiv.org/content/early/2023/07/07/2023.07.06.547759.short
4100  - http://biorxiv.org/content/early/2023/07/07/2023.07.06.547759.full
AB  - 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 StatementThe authors have declared no competing interest.