The design of highly-selective asymmetric catalysts is of great importance for the modern pharmaceutical industry and has been a research focus of the chemical community. In this part, we trained Reaxtica using the 1075-entry dataset (Figure ) originally from Denmark et al.'s work, in which they applied ASO and ESPMAX descriptors to represent steric and electronic effect, respectively, for predicting the enantioselectivity of asymmetric N,S-acetal formation reaction. Glorius et al. has used multiple fingerprint features (MFF) as a versatile representation to predict various reactivity problems including asymmetric N,S-acetal formation. In asymmetric catalytic reaction, the driving force for enantioselectivity is the asymmetric induction from the chiral catalyst. The hydroxyl group provides proton to activate the electrophile while the other oxygens with lone pairs of electrons may activate the nucleophile, and the rigidity of the substituted naphthalene may control the selectivity via geometric constraints. Considering the nature of the catalytic process, we first extracted 38 descriptors for the reaction, including those from qmdesc, TSEI, and B5 from Sterimol (highlighted atoms and bonds in Figure ). In addition to regular validation, the dataset was divided into three test sets for substrates (sub), catalysts (cat) and both (sub-cat) to assess the generalizability of the model. We analyzed the related descriptors using the 10 random 600:475 split. We calculated the corresponding change in MAE while removing each descriptor in turn. The most important descriptors are mainly about the naphthalene ring and the hydroxyl group of CPA (Figure ). In Denmark et al.'s work, the steric effect (ASO) of CPA played a dominant role in enantioselectivity while our model implied that the electronic effect matters most.