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bioRxiv · 10.64898/2026.03.18.712573

RNASTOP: A Deep Learning Framework for mRNA Chemical Stability Prediction and Optimization

Lin, S.·Chen, J.·Sun, H.·Zhang, Y.·Yang, W.·tan, h.·Wei, D.-Q.·Jiang, Q.·Xiong, Y.
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Abstract

Messenger RNA (mRNA) vaccines offer promising therapeutics for combating various diseases, yet their inherent chemical instability hampers their long-term efficacy. Although several methods have been developed to predict mRNA degradation, they exhibit limited accuracy and lack the capability for rational sequence optimization. Here, we propose RNASTOP, a novel framework integrating deep learning with heuristic search to simultaneously predict and optimize mRNA chemical stability. RNASTOP achieves a 13% accuracy improvement over the top-performing model on the Stanford OpenVaccine competition dataset and demonstrates robust generalization in predicting full-length mRNA degradation. Applied to mRNA codon optimization, RNASTOP reduces the minimum free energy of the Varicella-Zoster Virus vaccine sequence by 75.73% while maintaining high translation efficiency. Overall, RNASTOP serves as a powerful tool for predicting and optimizing mRNA chemical stability, poised to expedite the development of mRNA therapeutics. The source code of RNASTOP can be accessed at https://github.com/xlab-BioAI/RNASTOP.

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Lin, S., Chen, J., Sun, H., Zhang, Y., Yang, W., tan, h., Wei, D.-Q., Jiang, Q., Xiong, Y.. 2026-03-20. RNASTOP: A Deep Learning Framework for mRNA Chemical Stability Prediction and Optimization. https://doi.org/10.64898/2026.03.18.712573

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