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

TL;DR

This paper introduces a novel in-context learning approach with GPT models for personalized drug synergy prediction and design, enabling tailored cancer treatments without relying on traditional domain-specific data.

Contribution

It proposes new pre-training schemes for GPT to learn drug synergy functions and integrates genetic algorithms for optimizing drug combinations in personalized medicine.

Findings

Achieves competitive results without domain-specific knowledge

Enables personalized drug synergy prediction from small datasets

Explores inverse drug design for targeted therapies

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.