arXiv cs.AI· Zijie Liu, Hongxuan Li, Zhen Tan, Jinhao Duan, Baixiang Huang, Zunpeng Liu, Kai Shu, Tianlong Chen·· 13 小时前AI 评分26
DrugReason：融合知识图谱与语言证据的多视图推理框架，用于药物重定位
DrugReason: Dynamic Multi-View Reasoning over Knowledge Graph and Language Evidence for Drug Repurposing
AI 导读
DrugReason 是一个多视图推理框架，将知识图谱的多跳推理与 LLM 生成的机制推理解答相结合用于药物重定位，并按查询上下文自适应路由到专门专家，通过跨专家蒸馏实现知识共享。在 PharmaDB、DDInter 和 DrugBank 上，其平均表现优于强单视图推理基线，与基于图的替代方法相比达到有竞争力或更优的结果，并提供可解释的路由预测。该工作入选 EMNLP 2026 主会议。
正文 · 原文
Abstract:Drug repurposing aims to identify new therapeutic uses for existing compounds and, compared with de novo drug discovery, offers a faster and more cost-effective path to clinical translation. However, the space of candidate drug-disease pairs is enormous and their underlying relationships often depend on complex multi-hop biological mechanisms, making it difficult to reliably predict which pairs represent true therapeutic relationships. Existing approaches tackle this from two directions: knowledge graph-based methods organize curated biomedical evidence into structured relational networks for grounded multi-hop reasoning, while LLM-based methods leverage pretrained knowledge to generate flexible mechanistic rationales. Yet neither is sufficient alone - KGs are confined to observed graph structure while LLMs lack factual grounding and risk hallucination. To address this gap, we propose DrugReason, a multi-view reasoning framework that integrates grounded KG reasoning with LLM-generated mechanistic inference for drug repurposing. DrugReason adaptively routes diverse reasoning paths to specialized experts conditioned on the query context, while a cross-expert distillation objective enables knowledge sharing without sacrificing expert specialization. Experiments on PharmaDB, DDInter, and DrugBank show that DrugReason improves average performance over strong single-view reasoning baselines and achieves competitive or superior results compared with graph-based alternatives, while providing interpretable routing-based predictions.
| Comments: | EMNLP 2026 Main Conference Code: this https URL |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2609.06779 [cs.LG] |
| (or arXiv:2609.06779v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2609.06779 arXiv-issued DOI via DataCite |
Submission history
From: Zijie Liu [view email]
[v1]
Sun, 6 Sep 2026 18:54:26 UTC (1,280 KB)
[v2]
Tue, 20 Oct 2026 21:18:05 UTC (1,280 KB)
来源：arXiv cs.AI · arxiv.org