Improving drug repositioning with negative data labeling using large language models.

ABSTRACT:

Introduction

Drug repositioning offers numerous advantages, such as faster development timelines, reduced costs, and lower failure rates in drug development. Supervised machine learning is commonly used to score drug candidates but is hindered by the lack of reliable negative data-drugs that fail due to inefficacy or toxicity- which is difficult to access, lowering their prediction accuracy and generalization. Positive-Unlabeled (PU) learning has been used to overcome this issue by either randomly sampling unlabeled drugs or identifying probable negatives but still suffers from misclassification or oversimplified decision boundaries.

Results

We proposed a novel strategy using Large Language Models (GPT-4) to analyze all clinical trials on prostate cancer and systematically i

SUBMITTER: Picard M 

PROVIDER: S-EPMC11796214 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature