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

Authors

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

Published in

Briefings in bioinformatics. Volume 27. Issue 5. Sep 01, 2026.

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.

PMID:
42704264
Bibliographic data and abstract were imported from PubMed on 07 Sep 2026.

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