{
  "id": 3181305,
  "title": "CodonMamba: a foundation model for programmable mRNA coding sequence design",
  "url": "https://urgent.news/2026/08/24/codonmamba-a-foundation-model-for-programmable-mrna-coding-sequence",
  "topic": "science",
  "section": "Science",
  "published": "2026-08-24T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.08.24.746601v1?rss=1"
  },
  "original_language": "en",
  "account": "CodonMamba is a groundbreaking codon language model framework designed for mRNA prediction and programmable coding sequence (CDS) design. Trained on extensive coding-sequence data, CodonMamba outperformed existing models in 10 out of 12 benchmark mRNA prediction tasks. What sets CodonMamba apart is its ability to transform codon optimization from a trained-inference-dependent process to an inference-time steerable generation system. By allowing users to input codon usage preferences during generation, CodonMamba facilitates context-specific mRNA sequence engineering without the need for retraining. In experimental evaluations, CodonMamba demonstrated the capability to coordinate multiple design-relevant sequence properties and enable programmable cross-host CDS retargeting through simple prior switches during inference. This innovative approach preserves the majority of model-derived codon selections while offering unprecedented flexibility for precise mRNA design using foundation models.",
  "summary": "Although mRNA codon language models provide a generalizable framework for biological sequence design, effective CDS design requires both a learned sequence design space that captures biological constraints and context-configurable design preferences. Here we present CodonMamba, a codon language model framework for mRNA prediction and programmable CDS design. Pretrained on large-scale…",
  "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."
}