The enumerated library consisted of 26,350 potential molecules generated from 125 selected MAGLinhibiting molecules featuring N -heteroaromatic fragments and 211 carboxylic acids. Each molecule was labeled as positive or negative on the basis of predicted reaction outcomes: molecules with a predicted reaction yield of ≥ 5% were classified as positive, while those with lower predicted yields were classified as negative. The final predictions for each molecule were based on the mean of three models, providing a robust assessment for subsequent machine learning model training and reaction property predictions.