MixRx: Predicting Drug Combination Interactions with LLMs
This work addresses drug interaction prediction for medical applications, but it is incremental as it applies existing LLMs to a new biological task.
The paper tackles the problem of classifying drug combination interactions using Large Language Models (LLMs), achieving an average accuracy of 81.5% with a fine-tuned Mistral Instruct 2.0 model.
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.