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.
Large Language Models Meet Biomedical Knowledge Graphs for Mechanistically Grounded Therapeutic Prioritization
National Library of Medicine (NLM), National Institutes of Health (NIH)（美国国立卫生研究院国家医学图书馆）；National Cancer Institute (NCI), National Institutes of Health (NIH)（美国国立卫生研究院国家癌症研究所）；Dartmouth Health（达特茅斯医疗系统）；Johns Hopkins School of Medicine（约翰斯·霍普金斯大学医学院）
CongFu: Conditional Graph Fusion for Drug Synergy Prediction
University of Toronto（多伦多大学）；Vector Institute for Artificial Intelligence（向量人工智能研究所）；The Hospital for Sick Children（病童医院）；University Health Network（大学健康网络）；Peter Munk Cardiac Centre, University Health Network（彼得·蒙克心脏中心，大学健康网络）
TxAgent: An AI Agent for Therapeutic Reasoning Across a Universe of Tools
Harvard Medical School（哈佛医学院）；MIT Lincoln Laboratory（麻省理工学院林肯实验室）；Harvard University（哈佛大学）；Broad Institute of MIT and Harvard（麻省理工学院与哈佛大学博德研究所）；Harvard Data Science Initiative（哈佛数据科学倡议）
Diffusion Models at the Drug Discovery Frontier: A Review on Generating Small Molecules versus Therapeutic Peptides
Xinjiang Key Laboratory of Biological Resources and Genetic Engineering（新疆生物资源与基因工程重点实验室）；College of Life Science and Technology, Xinjiang University（新疆大学生命科学与技术学院）；School of Mathematics and System Sciences, Xinjiang University（新疆大学数学与系统科学学院）
Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations
Interdisciplinary Program in Bioinformatics, Seoul National University（首尔国立大学生物信息学跨学科项目）；AIGENDRUG Co., Ltd.（爱真药物有限公司）；BK21 FOUR Intelligence Computing, Seoul National University（首尔国立大学BK21四号智能计算）；Interdisciplinary Program in Artificial Intelligence, Seoul National University（首尔国立大学人工智能跨学科项目）；Department of Artificial Intelligence, Inha University（仁荷大学人工智能系）
DeepCRE: Transforming Drug R&D via AI-Driven Cross-drug Response Evaluation
Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院（AIR））；School of Biological Science and Medical Engineering, Beihang University（北京航空航天大学生物与医学工程学院）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；School of Medicine, Tsinghua University（清华大学医学院）；Department of General Surgery, Peking University Third Hospital（北京大学第三医院普通外科）
Augmenting generative models with biomedical knowledge graphs improves targeted drug discovery
Edwardson School of Industrial Engineering and the Institute of Cancer Research, Purdue University（埃德沃兹工业工程学院和癌症研究所，普渡大学）；Elmore Family School of Electrical and Computer Engineering, Purdue University（埃尔摩家庭电气与计算机工程学院，普渡大学）
Decoding Drug Discovery: Exploring A-to-Z In silico Methods for Beginners
Charmo University（查尔莫大学）；Komar University of Science and Technology（科马尔科技大学）；University of Sulaimani（苏莱曼尼亚大学）；University of Kurdistan Hewler（库尔德斯坦休勒大学）；Eskisehir Osmangazi University（埃斯基谢希尔奥斯曼加齐大学）
Human genetic evidence is associated with drug approval across therapeutic areas: an observational analysis of 26,278 target-disease pairs with temporal validation and feature ablation
School of Informatics, University of Edinburgh（爱丁堡大学信息学院）
PharmAgents: Building a Virtual Pharma with Large Language Model Agents
Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院（AIR））；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；Department of Pharmaceutical Science, Peking University（北京大学药学院）；School of Future Technology, South China University of Technology（华南理工大学未来技术学院）
Foundation-Model-Boosted Multimodal Learning for fMRI-based Neuropathic Pain Drug Response Prediction
School of Computer Science, University of Sheffield（谢菲尔德大学计算机学院）；Centre for Machine Intelligence, University of Sheffield（谢菲尔德大学机器智能中心）；Academic Unit of Radiology, University of Sheffield（谢菲尔德大学放射学学术单元）；Diabetes Research Unit, Royal Hallamshire Hospital（皇家哈勒姆郡医院糖尿病研究单元）；Division of Clinical Medicine, University of Sheffield（谢菲尔德大学临床医学部）；Department of Computing, Imperial College London（伦敦帝国理工学院计算机系）
