{
  "id": 15132141,
  "title": "Benchmarking Large Language Models for Assay-Aware Drug-Target Binding Affinity Prediction",
  "url": "https://urgent.news/2026/10/16/benchmarking-large-language-models-for-assay-aware-drug-target",
  "topic": "science",
  "section": "Science",
  "published": "2026-10-16T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.10.11.758217v1?rss=1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Machine learning models for molecular discovery often train on bioactivity data from heterogeneous assays, yet assay context is rarely explicitly represented. We investigate whether natural-language assay descriptions provide predictive information beyond molecular and protein representations. We develop ACL-DTI (Assay-aware Contrastive Learning-based Drug Target Interaction) and MAMMAL-AsAP, an…",
  "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."
}