{
  "id": 16694360,
  "title": "AntiOxiPred: Predicting Antioxidant Peptides using Machine Learning Techniques and BLAST",
  "url": "https://urgent.news/2026/10/26/antioxipred-predicting-antioxidant-peptides-using-machine-learning",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-26T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.10.21.760867v1?rss=1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Antioxidant peptides (AOPs) offer significant therapeutic potential in counteracting oxidative damage associated with chronic diseases. As AOPs gain market traction in cosmeceuticals, functional foods, and clinical trials for disease intervention, efficient computational methods are essential to accelerate their discovery. In this study, a systematic attempt has been made to develop a highly…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 2,
    "also_reported_by": [
      {
        "outlet": "Scientific Reports",
        "title": "Predicting 1-year outcomes after anterior cruciate ligament reconstruction from preoperative physical performance using machine learning: an exploratory study",
        "url": "https://urgent.news/2026/10/27/predicting-1-year-outcomes-after-anterior-cruciate-ligament",
        "published": "2026-10-27T00:00:00.000Z"
      }
    ]
  },
  "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."
}