{
  "id": 13132623,
  "title": "Frustration Quenching and Network Topology of the Energy Landscape as Primary Determinants of Protein-Ligand Binding Pose Prediction by Deep Learning Models",
  "url": "https://urgent.news/2026/10/09/frustration-quenching-and-network-topology-of-the-energy-landscape-as",
  "topic": "science",
  "section": "Science",
  "published": "2026-10-09T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.10.05.756861v1?rss=1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Deep learning co-folding and docking models accurately predict ligand binding poses at orthosteric sites yet systematically underperform at allosteric pockets. Here, we demonstrate that this accuracy gap reflects a fundamental biophysical property of local energy landscape topology specifically, the magnitude of frustration quenching upon ligand binding rather than an intrinsic algorithmic…",
  "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."
}