To evaluate the soundness of our NNET model, two control experiments were conducted: Y-randomization, in which yields are randomly shuffled across the dataset; and a random data test, where chemically meaningful descriptors are replaced with randomly generated values . The low correlations observed (R 2 = 0.02) suggest that the predictions of our NNET model were achieved by identifying relationships between yield and chemically meaningful featurization, rather than by finding chance correlations. To test the model's ability to extrapolate beyond the template library, literature validation was conducted with an additional 26 examples of 6-endo radical cyclization from Reaxys ® and SciFinder ® ; these substrates contained functional groups that are not represented in the training or testing datasets, such as heteroatoms (N, O) within the formed 6-membered ring (see SI for details). We were pleased to find that reasonable correlation was observed, even though the model was not trained on these types of substrates. The lower correlation (R 2 = 0.63) and higher MAE (15.7%) are likely due to the different chemical reactivity between substrates possessing all-carbon skeletons and those with heteroatoms. For the purposes of clovane sesquiterpenoid synthesis, it was not necessary to have high performance for these substrate types, but the reasonable performance suggests our model provides synthetically useful predictions.