{
  "id": 10757144,
  "title": "Assessing chemical toxicity across Eukaryota using multimodal transformers",
  "url": "https://urgent.news/2026/09/29/assessing-chemical-toxicity-across-eukaryota-using-multimodal",
  "topic": "science",
  "section": "Science",
  "published": "2026-09-29T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.09.23.753795v1?rss=1"
  },
  "original_language": "en",
  "account": "Biodiversity worldwide faces threats from chemical pollution, yet comprehensive toxicity data are lacking for vast numbers of species and chemicals, severely hindering our understanding of ecological impacts. In this study, researchers introduce TRIDENT-2, an advanced artificial intelligence model designed to predict chemical toxicity across a wide range of eukaryotic species. This model is trained on an extensive dataset of 560,780 toxicity assays, encompassing 82,775 chemicals, 6,793 species, and various exposure scenarios.\n\nTRIDENT-2 demonstrates remarkable accuracy in predicting toxicity across the diverse group of Eukaryota, with an average median absolute error ranging from 1.76 to 3.80. The model achieves this accuracy by integrating information from chemical properties, biological characteristics, and experimental data. This multimodal approach allows TRIDENT-2 to maintain high predictive capability even when dealing with significant differences in chemical composition and taxonomic classifications, thus enabling the assessment of toxicity for species and chemicals that lack current experimental evidence.\n\nThe findings of this research highlight the potential of artificial intelligence to address longstanding data limitations in the field of ecotoxicology. By overcoming these challenges, TRIDENT-2 offers a promising tool for enhancing decision-making processes and mitigating chemical impacts on biodiversity and ecosystems. This advancement underscores the transformative role of AI in addressing critical environmental issues.",
  "summary": "Biodiversity is globally threatened by chemical pollution, yet toxicity data remain unavailable for millions of species and tens of thousands of chemicals, severely limiting our ability to assess ecological impacts. Here we present TRIDENT-2, a multimodal artificial intelligence model for predicting chemical toxicity across evolutionarily diverse eukaryotic species. Trained on 560,780 toxicity…",
  "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."
}