For completeness, a random forest regression model was also trained on the Mordredcalculated features for both quinones and hydroquinones, the results of which are shown in Figure . Both the random forest and ridge regression models with the Mordred-calculated features are susceptible to overfitting, due to the large number of features (∼ 1000) compared to the size of the datasets (quinones: ∼ 1100; hydroquinones ∼ 3200). We used a relatively high penalization term (α = 100) in the ridge regression to reduce the total magnitude of all the coefficients in the model (and thus mitigate overfitting). However, preliminary hyperparameter tuning in the random forest model did not result in significant model improvement, so they were left at the default values.