Elamur Research⌕
☰
home / papers / 2026 / Applicability Condition Extraction for Therapeutic Drug-Disease Relations

Applicability Condition Extraction for Therapeutic Drug-Disease Relations

Paper · 2026 · Guanting Luo, Noriki Nishida, Yuji Matsumoto et al.

Summary

Most biomedical relation extraction work only asks whether a drug treats a disease. It ignores the circumstances that make the treatment work, such as dose, patient group or coexisting illness. Luo et al. define a new task: given a drug–disease pair from a PubMed abstract, find the text spans that state these applicability conditions and assign each span a type. They build Drug-ACE from ChemDisGene. They keep only therapeutic relations backed by clinical studies, then have annotators mark condition spans, with input from a domain expert. They benchmark span-based extractors, LoRA-tuned LLMs and prompted LLMs. They also propose Role-Conditioned LoRA (RCLoRA), which gives the drug and disease roles their own low-rank adaptation. RCLoRA beats the strong baselines in every evaluation setting tested, and it adds only a few trainable parameters. The work matters because conditional treatment evidence is usually implicit and spread across sentences. Capturing it makes extracted knowledge more useful for clinical decision support. The authors stress that the dataset is a research tool, not validated medical truth.

Key points

  • Drug-ACE dataset: 1,119 annotated drug–disease pairs drawn from ChemDisGene PubMed abstracts. Annotation took place from October to December 2025. Code and data are at github.com/guantingluo98/Drug-ACE.
  • Condition types: there are six, chosen after a literature review and expert consultation: Dosage, Age, Gene, Gender, Comorbidity and Body Type (e.g., pregnancy, obesity).
  • Soft-matching results (Gemma3-4B backbone, threshold 0.5): RCLoRA scores 63.16 soft F1 for span plus type, against 57.45 for standard LoRA. It stays ahead at every threshold from 0.1 to 0.9.
  • Parameter cost (Gemma2-9B): the overhead is small, for example 28.4M versus 27.0M trainable parameters at rank 8. RCLoRA's advantage holds across ranks 4–32 and grows with rank.
  • Ablation (Qwen3-4B): inserting entity markers in place of role-specific LoRA hurts performance. Gene and Comorbidity remain the hardest condition types.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓The University of OsakaOsaka · Japan15 works · 517 citations🎓Tohoku UniversitySendai · Japan20 works · 514 citations🔬RIKENWako · Japan26 works · 526 citations🎓Institute of Science TokyoTokyo · Japan10 works · 20 citations

 Fields, topics and concepts

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksComputational Theory and Mathematics · 252 worksBiochemistry, Genetics and Molecular Biology · 234 worksMolecular Biology · 231 works

Cite

Cite as Guanting Luo, Noriki Nishida, Yuji Matsumoto and Yuki Arase. 2026. Applicability Condition Extraction for Therapeutic Drug-Disease Relations. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-acl.154

@inproceedings{luo2026applicability,
  title = {{Applicability Condition Extraction for Therapeutic Drug-Disease Relations}},
  author = {Guanting Luo, Noriki Nishida, Yuji Matsumoto and Yuki Arase},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.154},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.154}
}

References

46 references, 13 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Alan Akbik, Tanja Bergmann, Duncan Blythe, Kashif Rasul, Stefan Schweter, and Roland Vollgraf. 2019. FLAIR: An easy-to-use framework for state-of-the-art NLP. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations), pages…
  2. Arjun Bhatt, Ruth Roberts, XI Chen, Ting Li, Skylar Connor, Qais Hatim, Mike Mikailov, Weida Tong, and Zhichao Liu. 2021. Dice: A drug indication classification and encyclopedia for ai-based indication extraction. Frontiers in Artificial Intelligence, 4:711467. find
  3. Stephen Bonner, Ian P Barrett, Cheng Ye, Rowan Swiers, Ola Engkvist, Andreas Bender, Charles Tapley Hoyt, and William L Hamilton. 2022. A review of biomedical datasets relating to drug discovery: a knowledge graph perspective. Briefings in Bioinformatics, 23(6):bbac404. find
  4. DeepSeek-AI, Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, Xiaokang Zhang, Xingkai Yu, Yu Wu, Z. F. Wu, Zhibin Gou, Zhihong Shao, Zhuoshu Li, Ziyi Gao, and 181 others. 2025. Deepseek-r1: Incentivizing reasoning capability in ll…
  5. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies,…
  6. Karel D’Oosterlinck, François Remy, Johannes Deleu, Thomas Demeester, Chris Develder, Klim Zaporojets, Aneiss Ghodsi, Simon Ellershaw, Jack Collins, and Christopher Potts. 2023. BioDEX: Large-scale biomedical adverse drug event extraction for real-world pharmacovigilance. In Findings of the Associat… find
  7. Ilseyar Alimova and Elena Tutubalina. 2019. Detecting adverse drug reactions from biomedical texts with neural networks. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 415– 421, Florence, Italy. Association for Computation… find
  8. Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, Amy Yang, Angela Fan, Anirudh Goyal, Anthony Hartshorn, Aobo Yang, Archi Mitra, Archie Sravankumar, Artem Korenev, Arthur Hinsvark, and 542 o… — The Llama 3 Herd of Models
  9. Emily Alsentzer, John Murphy, William Boag, Wei-Hung Weng, Di Jindi, Tristan Naumann, and Matthew McDermott. 2019. Publicly available clinical BERT embeddings. In Proceedings of the 2nd Clinical Natural Language Processing Workshop, pages 72–78, Minneapolis, Minnesota, USA. Association for Computati…
  10. Yu Gu, Robert Tinn, Hao Cheng, Michael Lucas, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, and Hoifung Poon. 2021. Domain-specific language model pretraining for biomedical natural language processing. ACM Transactions on Computing for Healthcare (HEALTH), 3(1):1–23.
  11. Gunjan Balde, Soumyadeep Roy, Mainack Mondal, and Niloy Ganguly. 2024. Medvoc: Vocabulary adaptation for fine-tuning pre-trained language models on medical text summarization. In IJCAI, pages 6180– 6188. find
  12. Imane Guellil, Salomé Andres, Atul Anand, Bruce Guthrie, Huayu Zhang, Abul Hasan, Honghan Wu, and Beatrice Alex. 2025. Adverse event extraction from discharge summaries: A new dataset, annotation scheme, and initial findings. In Proceedings of the 63rd Annual Meeting of the Association for Computati… find
  13. Ridong Han, Chaohao Yang, Tao Peng, Prayag Tiwari, Xiang Wan, Lu Liu, and Benyou Wang. 2024. An empirical study on information extraction using large language models. Preprint, arXiv:2305.14450. — [arXiv:2305.14450](https://arxiv.org/abs/2305.14450) find
  14. Peter Hanlon, Elaine W Butterly, Anoop SV Shah, Laurie J Hannigan, Jim Lewsey, Frances S Mair, David M Kent, Bruce Guthrie, Sarah H Wild, Nicky J Welton, and 1 others. 2023. Treatment effect modification due to comorbidity: Individual participant data meta-analyses of 120 randomised controlled trial… find
  15. Xinyi He, Yihao Liu, Mengyu Zhou, Yeye He, Haoyu Dong, Shi Han, Zejian Yuan, and Dongmei Zhang. 2025. TableLoRA: Low-rank adaptation on table structure understanding for large language models. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long…
  16. Sam Henry, Kevin Buchan, Michele Filannino, Amber Stubbs, and Ozlem Uzuner. 2020. 2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records. Journal of the American Medical Informatics Association, 27(1):3–12. find
  17. Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation, 9(8):1735– 1780. find
  18. Dongqi Liu and Vera Demberg. 2024. RST-LoRA: A discourse-aware low-rank adaptation for long document abstractive summarization. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), p… find
  19. Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. Preprint, arXiv:1907.11692.
  20. Zhiyong Lu. 2011. Pubmed and beyond: a survey of web tools for searching biomedical literature. Database, 2011:baq036. find
  21. Ling Luo, Po-Ting Lai, Chih-Hsuan Wei, Cecilia N Arighi, and Zhiyong Lu. 2022. Biored: a rich biomedical relation extraction dataset. Briefings in Bioinformatics, 23(5):bbac282. find
  22. Antonio Miranda-Escalada, Farrokh Mehryary, Jouni Luoma, Darryl Estrada-Zavala, Luis Gasco, Sampo Pyysalo, Alfonso Valencia, and Martin Krallinger. 2023. Overview of drugprot task at biocreative vii: data and methods for large-scale text mining and knowledge graph generation of heterogenous chemical… find
  23. Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, and 1 others. 2022. Lora: Low-rank adaptation of large language models. ICLR, 1(2):3.
  24. Dat Quoc Nguyen and Karin Verspoor. 2018. Convolutional neural networks for chemical-disease relation extraction are improved with character-based word embeddings. In Proceedings of the BioNLP 2018 workshop, pages 129–136, Melbourne, Australia. Association for Computational Linguistics. find
  25. Yifan Jin, Jiangmeng Li, Zheng Lian, Chengbo Jiao, and Xiaohui Hu. 2022. Supporting medical relation extraction via causality-pruned semantic dependency forest. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2450– 2460, Gyeongju, Republic of Korea. Internatio… find
  26. Arpita Roy and Shimei Pan. 2021. Incorporating medical knowledge in BERT for clinical relation extraction. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 5357–5366, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics. find
  27. John Lafferty, Andrew McCallum, and Fernando Pereira. 2001. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proceedings of ICML, volume 1, pages 282–289. find
  28. Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang. 2020. Biobert: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4):1234–1240.
  29. Jiao Li, Yueping Sun, Robin J Johnson, Daniela Sci-aky, Chih-Hsuan Wei, Robert Leaman, Allan Peter Davis, Carolyn J Mattingly, Thomas C Wiegers, and Zhiyong Lu. 2016. Biocreative v cdr task corpus: a resource for chemical disease relation extraction. find
  30. Pranab Sahoo, Ayush Singh, Sriparna Saha, Aman Chadha, and Samrat Mondal. 2024. Enhancing adverse drug event detection with multimodal dataset: Corpus creation and model development. In Findings of the Association for Computational Linguistics: ACL 2024, pages 11214–11226, Bangkok, Thailand. Associa… find
  31. Andrew Sellergren, Sahar Kazemzadeh, Tiam Jaroensri, Atilla Kiraly, Madeleine Traverse, Timo Kohlberger, Shawn Xu, Fayaz Jamil, Cían Hughes, Charles Lau, Justin Chen, Fereshteh Mahvar, Liron Yatziv, Tiffany Chen, Bram Sterling, Stefanie Anna Baby, Susanna Maria Baby, Jeremy Lai, Samuel Schmidgall, a… find
  32. Daniel N. Sosa, Rogier Hintzen, Betty Xiong, Alex de Giorgio, Julien Fauqueur, Mark Davies, Jake Lever, and Russ B. Altman. 2023. Associating biological context with protein-protein interactions through text mining at pubmed scale. Journal of Biomedical Informatics, 145:104474. find
  33. Saurabh Srivastava, Sweta Pati, and Ziyu Yao. 2025. Instruction-tuning LLMs for event extraction with annotation guidelines. In Findings of the Association for Computational Linguistics: ACL 2025, pages 13055–13071, Vienna, Austria. Association for Computational Linguistics. find
  34. Gemma Team, Aishwarya Kamath, Johan Ferret, Shreya Pathak, Nino Vieillard, Ramona Merhej, Sarah Perrin, Tatiana Matejovicova, Alexandre Ramé, Morgane Rivière, Louis Rouillard, Thomas Mesnard, Geoffrey Cideron, Jean bastien Grill, Sabela Ramos, Edouard Yvinec, Michelle Casbon, Etienne Pot, Ivo Penche… find
  35. Gemma Team, Morgane Riviere, Shreya Pathak, Pier Giuseppe Sessa, Cassidy Hardin, Surya Bhupatiraju, Léonard Hussenot, Thomas Mesnard, Bobak Shahriari, Alexandre Ramé, and 1 others. 2024. Gemma 2: Improving open language models at a practical size. arXiv preprint arXiv:2408.00118. find
  36. Minjia Wang, Fangzhou Liu, Xiuxing Li, Bowen Dong, Zhenyu Li, Tengyu Pan, and Jianyong Wang. 2024. Bio-RFX: Refining biomedical extraction via advanced relation classification and structural constraints. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages… find
  37. Chih-Hsuan Wei, Yifan Peng, Robert Leaman, Allan Peter Davis, Carolyn J Mattingly, Jiao Li, Thomas C Wiegers, and Zhiyong Lu. 2016. Assessing the state of the art in biomedical relation extraction: overview of the biocreative v chemical-disease relation (cdr) task. Database, 2016:baw032. find
  38. An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, Chujie Zheng, Dayiheng Liu, Fan Zhou, Fei Huang, Feng Hu, Hao Ge, Haoran Wei, Huan Lin, Jialong Tang, and 41 others. 2025a. Qwen3 technical report. Preprint, arXiv:2505.09388.
  39. An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, Huan Lin, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang, Jiaxi Yang, Jingren Zhou, Junyang Lin, Kai Dang, and 23 others. 2025b. Qwen2.5 technical report. Preprint, arXiv:24… find
  40. Deming Ye, Yankai Lin, Peng Li, and Maosong Sun. 2022. Packed levitated marker for entity and relation extraction. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4904–4917, Dublin, Ireland. Association for Computational Lingu…
  41. Dongxu Zhang, Sunil Mohan, Michaela Torkar, and Andrew McCallum. 2022. A distant supervision corpus for extracting biomedical relationships between chemicals, diseases and genes. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 1073–1082, Marseille, France. Europe… find
  42. Qingru Zhang, Minshuo Chen, Alexander Bukharin, Pengcheng He, Yu Cheng, Weizhu Chen, and Tuo Zhao. 2023. Adaptive budget allocation for parameter-efficient fine-tuning. In The Eleventh International Conference on Learning Representations. find
  43. Haodong Zhao, Ruifang He, Mengnan Xiao, and Jing Xu. 2023. Infusing hierarchical guidance into prompt tuning: A parameter-efficient framework for multi-level implicit discourse relation recognition. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1:…
  44. Richard M Weinshilboum and Liewei Wang. 2017. Pharmacogenomics: precision medicine and drug response. In Mayo Clinic Proceedings, volume 92, pages 1711–1722. Elsevier. find
  45. Ye Wu, Ruibang Luo, Henry CM Leung, Hing-Fung Ting, and Tak-Wah Lam. 2019. Renet: A deep learning approach for extracting gene-disease associations from literature. In International Conference on Research in Computational Molecular Biology, pages 272–284. Springer. find
  46. Yan Xiao, Yaochu Jin, and Kuangrong Hao. 2024. Federated document-level biomedical relation extraction with localized context contrast. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 7163–7173, Torin… find

Updated

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/luo-2026-applicability-condition-extraction-therapeutic-drug/