| --- |
| license: apache-2.0 |
| language: |
| - en |
| tags: |
| - medical |
| size_categories: |
| - 1K<n<10K |
| task_categories: |
| - text-classification |
| --- |
| |
| This is the official dataset for the paper **"Applicability Condition Extraction for Therapeutic Drug-Disease Relations"**, accepted by **ACL 2026 Findings**. |
|
|
| ## Summary |
|
|
| Identifying conditions that a certain drug takes therapeutic effect on a target disease is crucial for clinical decision-making support. However, most existing biomedical information extraction methods have focused on identifying only relations between drugs and diseases, while largely overlooking the context-specific conditions where such relations can apply. To address this problem, we introduce the task of applicability condition extraction for therapeutic drug--disease relations from biomedical research literature. We create the first dataset that has manually annotated triples of drugs, diseases, and applicability conditions on biomedical paper abstracts with 1,119 drug-disease pairs. Using this dataset, we systematically evaluate the performance of a range of existing methods. In addition, we propose a new method that enhances LoRA to consider relations between drugs and diseases. Our method consistently outperforms strong baselines across different evaluation settings. |
|
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| Paper: https://aclanthology.org/2026.findings-acl.154/ |
|
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| Code: https://github.com/guantingluo98/Drug-ACE |
|
|
| ## Citation |
|
|
| If you find our paper, code, or dataset helpful, please consider citing our work: |
|
|
| ```bibtex |
| @inproceedings{luo-etal-2026-applicability, |
| title = "Applicability Condition Extraction for Therapeutic Drug-Disease Relations", |
| author = "Luo, Guanting and |
| Nishida, Noriki and |
| Matsumoto, Yuji and |
| Arase, Yuki", |
| editor = "Liakata, Maria and |
| Moreira, Viviane P. and |
| Zhang, Jiajun and |
| Jurgens, David", |
| booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026", |
| month = jul, |
| year = "2026", |
| address = "San Diego, California, United States", |
| publisher = "Association for Computational Linguistics", |
| url = "https://aclanthology.org/2026.findings-acl.154/", |
| pages = "3135--3148", |
| ISBN = "979-8-89176-395-1", |
| abstract = "Identifying conditions that a certain drug takes therapeutic effect on a target disease is crucial for clinical decision-making support. However, most existing biomedical information extraction methods have focused on identifying only relations between drugs and diseases, while largely overlooking the context-specific conditions where such relations can apply. To address this problem, we introduce the task of applicability condition extraction for therapeutic drug{--}disease relations from biomedical research literature. We create the first dataset that has manually annotated triples of drugs, diseases, and applicability conditions on biomedical paper abstracts with 1,119 drug-disease pairs. Using this dataset, we systematically evaluate the performance of a range of existing methods. In addition, we propose a new method that enhances LoRA to consider relations between drugs and diseases. Our method consistently outperforms strong baselines across different evaluation settings." |
| } |
