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

Jesús G. Estrada, Derek T. Ahneman, Robert P. Sheridan, Spencer D. Dreher, Abigail G. Doyle

Research output: Contribution to journalArticle

10 Scopus citations

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.

Original languageEnglish (US)
Article numbereaat8763
JournalScience
Volume362
Issue number6416
DOIs
StatePublished - Nov 16 2018

All Science Journal Classification (ASJC) codes

  • General

Fingerprint Dive into the research topics of 'Response to Comment on “Predicting reaction performance in C–N cross-coupling using machine learning”'. Together they form a unique fingerprint.

  • Cite this