Back

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.

2026-03-20 bioinformatics
10.64898/2026.03.18.712573 bioRxiv
Show 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.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

1
Briefings in Bioinformatics
◐ 354 papers in training set
Top 0.5%
10.7%
Similar papers in this journal
2
PLOS Computational Biology
● 1863 papers in training set
Top 4%
8.6%
Similar papers in this journal
3
Bioinformatics
◐ 1204 papers in training set
Top 3%
7.0%
Similar papers in this journal
4
Nucleic Acids Research
◐ 1281 papers in training set
Top 3%
6.5%
Similar papers in this journal
5
Molecular Therapy Nucleic Acids
○ 39 papers in training set
Top 0.1%
6.5%
Similar papers in this journal
6
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 0.4%
6.1%
7
Advanced Science
○ 286 papers in training set
Top 1%
5.0%
50% of probability mass above
8
Nature Communications
○ 5641 papers in training set
Top 29%
5.0%
9
Nature Machine Intelligence
○ 70 papers in training set
Top 0.6%
4.7%
10
Genome Biology
○ 637 papers in training set
Top 4%
3.1%
11
Genomics, Proteomics & Bioinformatics
188 papers in training set
Top 0.7%
2.7%
12
Bioinformatics Advances
◐ 203 papers in training set
Top 2%
2.6%
13
NAR Genomics and Bioinformatics
◐ 242 papers in training set
Top 2%
2.1%
14
Nature Methods
○ 385 papers in training set
Top 4%
1.7%
15
Journal of Chemical Information and Modeling
● 238 papers in training set
Top 2%
1.7%
16
NAR Molecular Medicine
◐ 22 papers in training set
Top 0.1%
1.5%
17
RNA
● 189 papers in training set
Top 1.0%
1.4%
18
Scientific Reports
○ 3612 papers in training set
Top 61%
1.3%
19
International Journal of Molecular Sciences
○ 494 papers in training set
Top 12%
1.1%
20
iScience
○ 1154 papers in training set
Top 29%
1.1%
21
Cell Systems
○ 201 papers in training set
Top 4%
1.1%
22
Computers in Biology and Medicine
○ 128 papers in training set
Top 4%
1.1%
23
Journal of Molecular Biology
○ 232 papers in training set
Top 3%
1.0%
24
RNA Biology
○ 78 papers in training set
Top 1%
0.9%
25
ACS Omega
● 105 papers in training set
Top 3%
0.9%
26
BMC Bioinformatics
○ 457 papers in training set
Top 6%
0.8%
27
Journal of Chemical Theory and Computation
● 140 papers in training set
Top 1%
0.8%
28
Cell Reports Methods
○ 165 papers in training set
Top 4%
0.8%
29
PLOS ONE
● 5266 papers in training set
Top 60%
0.8%
30
Genome Research
● 468 papers in training set
Top 7%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.