We used 268 reactions of 181 sequence sets as the training data for our model and the recurrent neural network-based deep learning model (DLM). The architecture and training method of the DLM were referenced from the literature. In the test set composed of 81 reactions of 48 sequence sets, these two models performed closely (Figure ). Specifically, The F2acc, F3acc and RMSE of our model are close to the DLM (56.8% versus 57.9%, 80.2% versus 81.5%, 0.39 versus 0.394). The results indicate that the prediction performance of our model is comparable to that of the DLM, which proves the capacity of deep learning models to generate accurate predictions when provided with adequate data.