The results indicate that models performed better on the random split and partial substrate novelty test sets compared to the full substrate novelty test set. This could be inferred from the lower MAE and RMSE values and higher R 2 values for the first two splits (shown in Table ). These findings align with our expectations that models trained on datasets where they are exposed to a broad range of substrates and reaction conditions perform better on familiar substrates, but their performance drops when predicting yields for entirely new substrate pairs.