Applicability Condition Extraction for Therapeutic Drug-Disease Relations

Computer Science > Artificial Intelligence [Submitted on 12 Jun 2026] Applicability Condition Extraction for Therapeutic Drug-Disease Relations Guanting Luo, Noriki Nishida, Yuji Matsumoto, Yuki Arase 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. The source code and dataset of this paper can be obtained from: this https URL Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2606.14031 [cs.AI]   (or arXiv:2606.14031v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2606.14031 Focus to learn more Submission history From: Guanting Luo [view email] [v1] Fri, 12 Jun 2026 02:10:57 UTC (185 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)