Evidence map›Paper›PMID 42704264›Full record

ArticleBriefings in bioinformatics2026

CAREPath: semantic context-aware reasoning paths with mechanism-augmented embeddings for drug repurposing.

Haerin Song, Dongmin Bang, Bonil Koo, Sun Kim, Sangseon Lee

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Article in Briefings in bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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5 authors.

Haerin SongInterdisciplinary Program in Artificial Intelligence, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea.ORCID 0009-0008-3199-0138
Dongmin BangInterdisciplinary Program in Bioinformatics, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea.ORCID 0000-0001-9217-8380
Bonil KooInterdisciplinary Program in Bioinformatics, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea.ORCID 0000-0003-4357-1850
Sun KimInterdisciplinary Program in Artificial Intelligence, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea.ORCID 0000-0001-5385-9546
Sangseon LeeDepartment of Artificial Intelligence, Inha University, 100 Inha-ro, michuhol-gu, Incheon 22180, Republic of Korea.ORCID 0000-0002-7398-9580

Funding

AIGENDRUG Co., LtdMinistry of Trade, Industry & Resources RS-2025-13642970National Research Foundation of Korea RS-2023-NR077172National Research Foundation of Korea RS-2026-25473777National Research Foundation of Korea RS-2026-25516068National Research Foundation of Korea RS-2026-25518860
6 · The paper itself

Abstract

Biomedical knowledge graphs that include drugs, genes, and diseases support drug repurposing by connecting drugs to diseases through gene-mediated multi-hop paths, thereby enabling mechanism-of-action reasoning. However, deeper traversal does not necessarily improve mechanistic reasoning: long paths grow combinatorially and frequently pass through hub genes, producing irrelevant gene regulatory signals, whereas overly constrained or sparse paths may miss broader biological context. We propose Context-Aware REasoning Path (CAREPath), a knowledge graph (KG)-large language model framework inspired by depth-search and breadth-search reasoning to balance mechanistic specificity, scalability, and context recovery. The depth-search strategy constrains traversal to short disease-gene-drug paths, converts each path into a structured prompt, and encodes it with a biomedical language model to generate semantic path embeddings. Complementarily, the breadth-search strategy constructs entity-level mechanism-context embeddings from one-hop gene neighborhoods and enriches them through similarity-guided augmentation using pharmacologically related drugs and gene-signature-similar diseases. Across five biomedical KGs, CAREPath achieves the best area under the precision-recall curve (AUPRC) in the disease cold-start setting among 18 baselines, improving performance by up to 3.6%. Additional analyses show that semantic short-path encoding contributes most to performance, while mechanism-context augmentation improves robustness under sparse path signals and strengthens gene ontology functional agreement. Case studies and recently U.S. Food and Drug Administration (FDA)-approved indications further demonstrate its practical relevance, positioning CAREPath as a framework that supplies interpretable mechanistic rationales where constrained path is available, while remaining robust when it is not. Source code is available at https://github.com/hamppy-song/CAREPath.

Indexed as

Computational BiologyDrug RepositioningSemanticsAlgorithmsHumansLarge Language Modelsbiomedical knowledge graphdrug–disease associationdrug repurposinglarge language modelmechanism-of-action reasoningpath-based reasoning

Identifiers

PMID42704264
PMCPMC13548330

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.