In contrast to the template-based methods, template-free methods do not rely on reaction template libraries, learning the mapping from products to reactions directly. Typically, these methods build sequential models, e.g., the transformer-based Seq2Seq models , to predict the SMILES strings of reactants from products . Besides predicting SMILES strings, the Graph2SMILES first transforms the SMILES strings of products to graphs by RDKit and then builds conditional graph generative models to predict reactants' molecular graphs. These template-free methods often own better generalization power than template-based methods, which can predict some reactants unseen in the training data. However, without strict chemical constraints and sufficient training data, these methods often generate infeasible artifacts (i.e., fake molecules disobeying chemical laws).