To better depict the relative differences within a reaction, we calculated the difference to the average and added them into the reaction vector, which resulted in a 100-dimension vector for each product. We noticed that though most products of DA reactions are kinetically favored, there are some thermodynamic products under high temperature or catalyzed by Lewis acid. Therefore, we also took the temperature and Lewis acid into consideration. The ML model was trained on a 3040-entry reaction dataset for predicting regioselectivity, and a 3533-entry reaction dataset for predicting regioselectivity & site-selectivity, in which both datasets are from previous work of Grzybowski et al. (the detailed description of dataset could be found in the SI, Section S4). We evaluated different ML models and found RF performed best. Using the same reaction dataset provided by Grzybowski et al., Reaxtica showed similar performance compared to Grzybowski et al.'s model (94.0% vs. 93.6%) in 5-fold validation of regioselectivity prediction. As a test on generalizability, we extracted an external test dataset from Reaxys containing 219 regioselective or site-selective DA reactions published between 2019-01-01 to 2021-08-13 after filtering out repetitive reactions in the training dataset. Reaxtica achieved much better accuracies in the 219 external reactions than Grzybowski et al.'s model (93.6% vs. 73.1%), demonstrating the generality of Reaxtica (Figure ).