  Synthesis and efficient implementation mRNA strands has been shown to have
wide utility, especially recently in the development of COVID vaccines.
However, the intrinsic chemical stability of mRNA poses a challenge due to the
presence of 2'-hydroxyl groups in ribose sugars. The -OH group in the backbone
structure enables a base-catalyzed nucleophilic attack by the deprotonated
hydroxyl on the adjacent phosphorous and consequent self-hydrolysis of the
phosphodiester bond. As expected for in-line hydrolytic cleavage reactions, the
chemical stability of mRNA strands is highly dependent on external
environmental factors, e.g. pH, temperature, oxidizers, etc. Predicting this
chemical instability using a computational model will reduce the number of
sequences synthesized and tested through identifying the most promising
candidates, aiding the development of mRNA related therapies. This paper
proposes and evaluates three deep learning models (Long Short Term Memory,
Gated Recurrent Unit, and Graph Convolutional Networks) as methods to predict
the reactivity and risk of degradation of mRNA sequences. The Stanford Open
Vaccine dataset of 6034 mRNA sequences was used in this study. The training set
consisted of 3029 of these sequences (length of 107 nucleotide bases) while the
testing dataset consisted of 3005 sequences (length of 130 nucleotide bases),
in structured (Lowest Entropy Base Pair Probability Matrix) and unstructured
(Nodes and Edges) forms. The stability of mRNA strands was accurately
generated, with the Graph Convolutional Network being the best predictor of
reactivity ($RMSE = 0.249$) while the Gated Recurrent Unit Network was the best
at predicting risks of degradation ($RMSE = 0.266$). Combining all target
variables, the GRU performed the best with 76% accuracy. Results suggest these
models can be applied to understand and predict the chemical stability of mRNA
in the near future.
