AutoNovo: Agentic Program Search Discovers Transferable Nucleic Acid Design Algorithms

Key points

• In this work, we use agentic program search to iteratively propose, execute, evaluate, and revise code for nucleic-acid designers.1
• AutoNovo designers achieved the best observed mean among the evaluated methods on 3/3 DNA-design tasks in terms of in-silico performance, and 6/6 transfer tasks in terms of independent models.1
• We plan to release the resulting designers.1
• These results are the first steps in creating nucleic acid designers that maximize cross-model generalization, and thus improving the odds that in-silico designed sequences reflect real biology.1

Sources (1)

• [1]AutoNovo: Agentic Program Search Discovers Transferable Nucleic Acid Design Algorithms
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 06:08 AM

  In-silico optimization of nucleic acids relies on predictive oracles that imperfectly capture biological function, so sequences optimized against one model may exploit model-specific artifacts and fail to generalize to independent models.

  In this work, we use agentic program search to iteratively propose, execute, evaluate, and revise code for nucleic-acid designers.

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