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.
Paper: https://aclanthology.org/2026.findings-acl.154/
Code: https://github.com/guantingluo98/Drug-ACE
Citation
If you find our paper, code, or dataset helpful, please consider citing our work:
@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."
}