We found that the use of logistic regression-a probability-based regression algorithm that can provide insights into binary classifications for moderately sized data sets-allowed identification of the key structural features of the acids enabling high efficiency by defining the probability that a given carboxylic acid will provide a high product yield in the oxidation of S1 (20% yield threshold). Figure . Logistic regression modeling for the enantioselective desymmetrization of substrate S1 based on the carboxylic acids displayed in Figure . The acid data set was partitioned using the Kennard-stone split method into 80:20 training:validation set. Black and white points denote active and inactive acids, respectively (20% yield threshold). Red and blue denote active and inactive chemical space, respectively. Dashed lines denote 25% and 75% probability that a certain acid will be active. The solid black line denotes a 50% probability that the acid will be active.