Response to Comment on "Predicting reaction performance in C-N cross-coupling using machine learning".
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- Record sourced from PubMed, PMID 30442777.
- Also identified by DOI 10.1126/science.aat8763.
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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.
Medical subject headings
- Machine Learning
- Models, Chemical