MixRx: Predicting Drug Combination Interactions with LLMs

Risha Surana; Cameron Saidock; Hugo Chacon

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arXiv · 2601.03277

MixRx: Predicting Drug Combination Interactions with LLMs

Risha Surana·Cameron Saidock·Hugo Chacon

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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.

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q-bio.OT cs.AI cs.CL cs.LG

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Risha Surana, Cameron Saidock, Hugo Chacon. 2025-12-28. MixRx: Predicting Drug Combination Interactions with LLMs. https://arxiv.org/abs/2601.03277

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