{
  "id": 13009584,
  "title": "SIMORGH: Ensemble-Aware Geometric Deep Learning for Apo-State and Cryptic Ligand Binding Site Prediction",
  "url": "https://urgent.news/2026/10/08/simorgh-ensemble-aware-geometric-deep-learning-for-apo-state-and",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-08T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.10.02.756215v1?rss=1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Most computational methods identify protein-ligand binding sites from ligand-bound (holo) protein structures, where the binding pocket is already preorganized. Although convenient for benchmarking, this setting differs from the practical drug discovery scenario, in which binding sites must be inferred from ligand-free (apo) proteins. In fact, binding-competent conformations may represent only a…",
  "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."
}