{"data":{"event":{"id":"c23de1fd-c943-48c7-8048-fd2ec2e70092","slug":"scaling-network-medicine-with-llms-for-combinatorial-drug-repurposing-in-395d2fed4f","title":"\n\nScaling Network Medicine with LLMs for Combinatorial Drug Repurposing in ER+ Breast Cancer \n\n","short_summary":"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","full_description":"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.","ledger_type":"benefit","primary_domain_id":"c09546b6-10f8-48df-8347-0c1979416464","event_status":"provisional","event_date":"2026-09-20T00:00:00.000Z","discovery_date":"2026-09-20T00:00:00.000Z","first_published_date":"2026-09-20T00:00:00.000Z","last_reviewed_date":"2026-09-20T00:00:00.000Z","geographic_scope":"International","affected_population":null,"base_impact_tier":1,"base_score":"1.00","attribution_multiplier":"0.1000","evidence_multiplier":"0.1000","realization_multiplier":"0.2000","durability_multiplier":"0.5000","current_event_score":"0.001000","confidence_level":"low","score_explanation":"Auto-published from news ingest as a provisional placeholder. Score is conservative until a named release is identified and the record is rescored.","methodology_version_id":"8745541b-0f8e-4d5a-9b47-5291d3813a8d","original_methodology_version_id":"8745541b-0f8e-4d5a-9b47-5291d3813a8d","published_at":"2026-09-21T04:01:12.392Z","created_at":"2026-09-21T04:01:12.392Z","updated_at":"2026-09-21T04:01:12.392Z","flags":[],"domain_name":"Medicine & Health","domain_slug":"medicine-health","methodology_version":"0.2"},"contributions":[{"id":"0399bcc9-0457-4e9d-9fba-9be01205eee9","event_id":"c23de1fd-c943-48c7-8048-fd2ec2e70092","model_id":"535014f7-c941-4d39-aa9b-8f9c1d3d07b7","role_description":"Unspecified system mentioned or implied by a news item. Remap to a named release when identified.","attribution_multiplier":"0.1000","credit_share":"1.0000","contribution_score":"0.001000","attribution_rationale":"News ingest does not infer a named model from the publisher alone. Attribution stays unspecified until a release is identified.","attribution_confidence":"medium","first_used_date":"2026-09-20T00:00:00.000Z","model_version_if_known":null,"review_status":"approved","created_at":"2026-09-21T04:01:12.399Z","model_slug":"unspecified-ai-system","model_name":"Unspecified AI system","identity_class":"unknown","is_internal":false,"family_name":"Unspecified","family_slug":"unknown-unspecified","organization_name":"Unknown","organization_slug":"unknown"}],"sources":[{"id":"fe331537-0a1a-4c7b-99b9-1fbddb5bd033","event_id":"c23de1fd-c943-48c7-8048-fd2ec2e70092","url":"\nhttps://www.biorxiv.org/content/10.64898/2026.09.14.728753v1?rss=1\n","canonical_url":"\nhttps://www.biorxiv.org/content/10.64898/2026.09.14.728753v1?rss=1\n","source_type":"preprint","publisher":"bioRxiv","author":null,"publication_date":"2026-09-20T00:00:00.000Z","retrieved_at":"2026-09-21T04:01:12.406Z","title":"\n\nScaling Network Medicine with LLMs for Combinatorial Drug Repurposing in ER+ Breast Cancer \n\n","excerpt":"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-m","content_hash":null,"source_reliability_class":"medium","is_primary_source":true,"is_independent":true,"is_peer_reviewed":false,"archived_url":null,"created_at":"2026-09-21T04:01:12.406Z"}],"claims":[{"id":"d0a70f5e-2cd1-4077-9b51-04efe3922eb1","event_id":"c23de1fd-c943-48c7-8048-fd2ec2e70092","claim_text":"\n\nScaling Network Medicine with LLMs for Combinatorial Drug Repurposing in ER+ Breast Cancer \n\n","claim_type":"outcome","claim_status":"supported","confidence_score":null,"created_at":"2026-09-21T04:01:12.412Z","updated_at":"2026-09-21T04:01:12.412Z"}],"revisions":[{"id":"b9f4cbe9-e310-42db-99c3-c1cc8476f219","event_id":"c23de1fd-c943-48c7-8048-fd2ec2e70092","model_id":null,"previous_score":"0.000000","new_score":"0.001000","previous_factors":{},"new_factors":{"tier":1,"evidence":0.1,"base_score":1,"durability":0.5,"attribution":0.1,"realization":0.2},"change_reason":"Auto-published from news ingest.","trigger_type":"news_ingest","trigger_source_ids":null,"reviewer_id":null,"review_status":"published","created_at":"2026-09-21T04:01:12.419Z"}],"secondary":[]},"methodology_version":"0.1"}