用大模型预测药物联用效果，准确率达81.5%
MixRx: Predicting Drug Combination Interactions with LLMs
- 用大模型分析患者多药史，判断联用是协同、拮抗还是相加
- 微调后的Mistral模型在标准与扰动数据上平均准确率81.5%
- 为大模型用于生物预测提供新思路，适合医药研究者参考
MixRx利用大语言模型（LLMs）根据多药患者病史分类药物组合相互作用为协同、相加或拮抗。我们评估了4种模型：GPT-2、Mistral Instruct 2.0及其微调版本。结果表明该应用具有潜力，其中微调后的Mistral Instruct 2.0模型在标准和扰动数据集上的平均准确率为81.5%。本文旨在推动这一新兴研究方向，探索大模型在生物预测任务中的可行性。
原文摘要 · Abstract (English)
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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