Various deep learning (DL) models have also been used to tackle the reaction outcome prediction problem, which could be roughly classified as graph-based and SMILES-based methods. For example, Coley et al. established a graph neural network (GNN) called Weisfeiler-Lehman neural network (WLN) to predict reaction outcome. Based on WLN, Struble et al. achieved high accuracies in site selectivity for aromatic C-H functionalization reactions. Using QM-augmented GNN, Guan et al. successfully predicted the regioselectivity of different substitution reactions. Similarly, Stuyver and Coley applied the same model in competing E2/SN2 and aromatic substitution reactions. In addition, Liu et al. developed a GNN-based model named DeepReac+ in stereoselectivity and yield prediction using the HTE dataset. Regarding to SMILES-based models, Schwaller et al. have applied natural language processing technology in reaction outcomes predictions, which was also extended to the selectivity of carbohydrate reactions and reaction yield predictions. These DL-based models worked well in forward reaction outcome prediction in USPTO and HTE datasets. However, most of them are still black-box models that do not generalize well.