The different results might attribute to the input of different models or the fluctuation of our model training as the gap among different descriptors is marginal in this task. In the following process, we added the descriptors in the order of their effects on MAE to train our model and found the top 24 descriptors (the full list of features can be found in Table ) were well suited for the current enantioselectivity prediction (Figure ). In this way, we could identify and remove the redundant descriptors and finally Reaxtica achieved nearly the best performance compared to the former model of Denmark et al and MFF 17 in different data split scenarios (Table ) especially in the sub-cat test set (MAE = 0.223 kcal/mol), demonstrating the generality of our method.