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bioRxiv · 10.1101/2025.06.25.661500

codonGPT: Reinforcement learning on a generative language model optimizes RNA sequences under biological constraints

Rajbanshi, B.·Guruacharya, A.
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Abstract

Emerging generative models for biology focus on DNA, non-coding RNA, or proteins, ignoring information hidden in mRNA. Additionally, in protein engineering and mRNA therapeutics the design of mRNA sequences is still a challenge, lacking a clear framework. Here, we introduce and rigorously evaluate two novel methods: a foundational model for mRNA and a reinforcement learning mRNA design framework built on such a model. codonGPT is the first generative foundational language model trained directly on coding mRNA sequences. To solve the problem of synonymous constraints that are only unique to mRNA, we introduce a novel method of inference-time masking, along with house-keeping genes evaluation. For the first time, we also rigorously demonstrate, that for precise mRNA therapeutics design, reinforcement learning on such a model provides a clear framework for biological optimization. Our study introduces a novel foundational model for mRNA and a new reinforcement learning based paradigm for mRNA sequence design.

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Rajbanshi, B., Guruacharya, A.. 2025-06-27. codonGPT: Reinforcement learning on a generative language model optimizes RNA sequences under biological constraints. https://doi.org/10.1101/2025.06.25.661500

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