CancerGPT for Few Shot Drug Pair Synergy Prediction Using Large Pretrained Language Models

Authors

Tianhao Li
Sandesh Shetty
Advaith Kamath
Ajay Jaiswal
Xiaoqian Jiang
Ying Ding
Yejin Kim

Publication Date

2-19-2024

Journal

npj Digital Medicine

Abstract

Large language models (LLMs) have been shown to have significant potential in few-shot learning across various fields, even with minimal training data. However, their ability to generalize to unseen tasks in more complex fields, such as biology and medicine has yet to be fully evaluated. LLMs can offer a promising alternative approach for biological inference, particularly in cases where structured data and sample size are limited, by extracting prior knowledge from text corpora. Here we report our proposed few-shot learning approach, which uses LLMs to predict the synergy of drug pairs in rare tissues that lack structured data and features. Our experiments, which involved seven rare tissues from different cancer types, demonstrate that the LLM-based prediction model achieves significant accuracy with very few or zero samples. Our proposed model, the CancerGPT (with ~ 124M parameters), is comparable to the larger fine-tuned GPT-3 model (with ~ 175B parameters). Our research contributes to tackling drug pair synergy prediction in rare tissues with limited data, and also advancing the use of LLMs for biological and medical inference tasks.

Keywords

Machine learning, Virtual drug screening

DOI

10.1038/s41746-024-01024-9

PMID

38374445

PMCID

PMC10876664

PubMedCentral® Posted Date

2-19-2024

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes