{
  "id": 5313,
  "url": "https://arxiv.org/abs/2604.24506v1",
  "title": "MIMIC: A Generative Multimodal Foundation Model for Biomolecules",
  "summary": "Biological function emerges from coupled constraints across sequence, structure, regulation, evolution, and cellular context, yet most foundation models in biology are trained within one modality or for a fixed forward task. We present MIMIC, a generative multimodal foundation model trained on our newly curated and aligned dataset, LORE, linking nucleic acid, protein, evolutionary, structural, regulatory, and semantic/contextual modalities within partially observed biomolecular states. MIMIC use",
  "authors": "Siavash Golkar, Jake Kovalic, Irina Espejo Morales, Samuel Sledzieski, Minhuan Li, Ksenia Sokolova et al.",
  "category": "research",
  "topics": "regulation,biotech",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-27T14:10:01.000Z",
  "fetched_at": "2026-07-14T16:31:40.220Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5313",
  "original_url": "https://arxiv.org/abs/2604.24506v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}