MixRx: Predicting Drug Combination Interactions with LLMs
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866915712474284032 |
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| author | Surana, Risha Saidock, Cameron Chacon, Hugo |
| author_facet | Surana, Risha Saidock, Cameron Chacon, Hugo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_03277 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MixRx: Predicting Drug Combination Interactions with LLMs Surana, Risha Saidock, Cameron Chacon, Hugo Other Quantitative Biology Artificial Intelligence Computation and Language Machine Learning 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. |
| title | MixRx: Predicting Drug Combination Interactions with LLMs |
| topic | Other Quantitative Biology Artificial Intelligence Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2601.03277 |