Before revision:  as a method to predict the stability/reactivity and risk of degradation of sequences of RNA.  Reasonably accurate results were able to be 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 under various circumstances (RMSE = 0.266)  . Results suggest feasibility of applying such methods in mRNA vaccine research in the near future.
After revision:  as a method to predict the stability/reactivity and risk of degradation of sequences of RNA. These predictions can be very useful in the development of mRNA vaccines as they can reduce the number of sequences synthesized and tested by helping to identify the most promising candidates. Reasonably accurate results were able to be 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 under various circumstances (RMSE = 0.266) . Results suggest feasibility of applying such methods in mRNA vaccine research in the near future.