@article{bd96d0670a344f0a92e7425f003c4fdc,
title = "Response to Comment on “Predicting reaction performance in C–N cross-coupling using machine learning”",
abstract = "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.",
author = "Estrada, {Jes{\'u}s G.} and Ahneman, {Derek T.} and Sheridan, {Robert P.} and Dreher, {Spencer D.} and Doyle, {Abigail G.}",
note = "Funding Information: We thank M. K. Nielsen for initial design of the one-hot encoded dataset and helpful discussions. Funding: Supported by Princeton University and a Camille Dreyfus Teacher-Scholar Award. Competing interests: The authors declare no competing interests. Data and materials availability: All data used to produce the reported results and additional analyses can be found online at https://github.com/doylelab/rxnpredict/ Response. Publisher Copyright: {\textcopyright} 2017 The Authors.",
year = "2018",
month = nov,
day = "16",
doi = "10.1126/science.aat8763",
language = "English (US)",
volume = "362",
journal = "Science",
issn = "0036-8075",
publisher = "American Association for the Advancement of Science",
number = "6416",
}