Training mRNA Language Models Across 25 Species for $165
This analysis documents OpenMed's end-to-end pipeline for protein engineering, covering structure prediction, sequence design, and codon optimization. The team compared multiple transformer architectures for codon-level language modeling, with CodonRoBERTa-large-v2 achieving a perplexity of 4.10 and Spearman CAI correlation of 0.40. They scaled training to 25 species, producing 4 production models in 55 GPU-hours at a cost of $165. The article provides transparent results, architectural decisions, and runnable code, serving as a practical resource for researchers in protein AI.
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