Due to these advantages, we chose Chemprop for the selectivity model. For substrates identified as reactive, the selectivity model predicted specific chemical features indicative of selectivity. This task underwent similar performance evaluations, achieving an accuracy of 97.6% and an AUC of 98.1% (Figure and Section S4.2 of the Supporting Information). In this case, the graph neural network's directed-message passing mechanism proved effective in capturing intricate molecular features relevant to selectivity. Collectively, the consistent performance of ML models on the two tasks underscored the robustness of our integrated balanced dataset and the benefit of combining wet lab experimentation with literature data mining.