Drug repurposing can accelerate therapy discovery for ER+ breast cancer, but combination selection remains difficult. We developed an LLM-driven network medicine framework that extracts drug--target relationships from 595,122 PubMed abstracts, builds a cross-model consensus network, overlays it onto the KEGG estrogen signaling pathway, and enumerates complementary drug pairs. Candidate combinations are ranked by ComboRank, which aggregates pathway coverage, LLM consensus, RAG validation, cross-method agreement, and ClinicalTrials.gov precedent. The framework identified 166 significant pairs at
Current score
+0.00
1 base · Useful (tier 1 of 5, 1 pts)
× 0.1000 attribution · Minor documented assistance
× 0.1000 evidence · Firsthand or social claim
× 0.2000 realization · Proposed
× 0.5000 durability · Medium-term
Event-level product before credit split: 0.00
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What happened
Drug repurposing can accelerate therapy discovery for ER+ breast cancer, but combination selection remains difficult. We developed an LLM-driven network medicine framework that extracts drug--target relationships from 595,122 PubMed abstracts, builds a cross-model consensus network, overlays it onto the KEGG estrogen signaling pathway, and enumerates complementary drug pairs. Candidate combinations are ranked by ComboRank, which aggregates pathway coverage, LLM consensus, RAG validation, cross-method agreement, and ClinicalTrials.gov precedent. The framework identified 166 significant pairs at FDR <= 0.05, including 31 with clinical-trial precedent, with 393 shared drug--target pairs across extraction strategies and 11 exact pairs additionally supported by the pathway overlay.
