Response to Comment on "Predicting reaction performance in C-N cross-coupling using machine learning"

We demonstrate that the chemical-feature model described in our original paper is distinguishable from the nongeneralizable models introduced by Chuang and Keiser. Furthermore, the chemical-feature model significantly outperforms these models in out-of-sample predictions, justifying the use of chemical featurization from which machine learning models can extract meaningful patterns in the dataset, as originally described. In Ahneman et al. (1), we showed that a random forest (RF) algorithm built using computationally derived chemical descriptors for the components of a Pd-catalyzed C–N cross-coupling reaction (aryl halide, ligand, base, and potentially inhibitory isoxazole additive) could identify predictive and meaningful relationships in a multidimensional chemical dataset comprising 4608 reactions. Chuang and Keiser (2) built alternative models using random barcode features ("straw" models), wherein the chemical descriptors are replaced with random numbers selected from a standard normal distribution. One-hot encoded features, wherein each reagent acts as a categorical descriptor and is marked as absent or present, were also evaluated.