MixRx: Predicting Drug Combination Interactions with LLMs

TL;DR

This paper explores the use of Large Language Models to classify drug interaction types from patient histories, demonstrating promising accuracy and potential for biological prediction tasks.

Contribution

It evaluates multiple LLMs, including fine-tuned models, for predicting drug interaction types, highlighting the feasibility of LLMs in pharmacological predictions.

Findings

Mistral Instruct 2.0 Fine-Tuned achieved 81.5% accuracy.

LLMs show potential for biological prediction tasks.

The study compares four different LLMs for this application.

Abstract

MixRx uses Large Language Models (LLMs) to classify drug combination interactions as Additive, Synergistic, or Antagonistic, given a multi-drug patient history. We evaluate the performance of 4 models, GPT-2, Mistral Instruct 2.0, and the fine-tuned counterparts. Our results showed a potential for such an application, with the Mistral Instruct 2.0 Fine-Tuned model providing an average accuracy score on standard and perturbed datasets of 81.5%. This paper aims to further develop an upcoming area of research that evaluates if LLMs can be used for biological prediction tasks.