{
  "id": 9704125,
  "title": "Deciphering Mechanistic Signatures in Drug-Drug Interactions with Dual Topology Graphs",
  "url": "https://urgent.news/2026/09/24/deciphering-mechanistic-signatures-in-drug-drug-interactions-with",
  "topic": "science",
  "section": "Science",
  "published": "2026-09-24T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.09.23.753684v1?rss=1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Drug-drug interactions (DDIs) represent a critical challenge in drug development and clinical practice, as they can lead to severe adverse effects, including toxicity and reduced therapeutic efficacy. Deep learning methods have shown promise in large-scale, rapid DDI prediction; however, current approaches suffer from significant limitations in providing mechanistic insights into these…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}