ArticlebioRxiv : the preprint server for biology2025
A generalized and efficient approach for complete mRNA design improves translation, stability and specificity.
Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
End-to-end, machine-learning based design of mRNA molecules offers a powerful means to tailor their properties for specific tasks. mRNA expression level, immunogenicity, tissue specificity, stability, and localization, among others strongly depend on sequence, thus providing a rich set of properties amenable to optimization. Despite this potential, the various components of mRNA are governed by distinct grammatical and functional rules that hinder the development of a unified algorithmic approach for complete mRNA design. While machine learning and generative AI techniques demonstrate substantial benefits for individual sequence design tasks, adapting these tools to new domains or out-of-distribution tasks remains challenging. In this work, we describe a simple and powerful alteration to integrated gradients (
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