mRNA vaccines are receiving increased interest as potential alternatives to conventional methods for the prevention of several diseases, including Covid-19. This paper proposes and evaluates three deep learning models (Long Short Term Memory networks, Gated Recurrent Unit networks, and Graph Convolutional Networks) 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.
mRNA vaccines are receiving increased interest as potential alternatives to conventional methods for the prevention of several diseases, including Covid-19. This paper proposes and evaluates three deep learning models (Long Short Term Memory networks, Gated Recurrent Unit networks, and Graph Convolutional Networks) 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) . Overall, combining all target variables, the GRU performed the best with an accuracy value of 76\% . Results suggest feasibility of applying such methods in mRNA vaccine research in the near future.
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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.
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. Overall, combining all target variables, the GRU performed the best with an accuracy value of 76\%