{
  "id": 7461756,
  "title": "How effective are contrastive learning-based approaches for activity-cliff prediction?",
  "url": "https://urgent.news/2026/09/14/how-effective-are-contrastive-learning-based-approaches-for-activity",
  "topic": "science",
  "section": "Science",
  "published": "2026-09-14T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.09.08.750257v1?rss=1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Activity cliffs, defined as structurally similar molecules with vastly different properties represent a fundamental challenge in modern day drug discovery for property prediction models. While Graph Neural Networks (GNNs) have advanced molecular property prediction, they inherently struggle with this problem due to representation collapse and node over smoothening. In this work, we evaluate the…",
  "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."
}