Predicting reaction performance in C–N cross-coupling using machine learning

By Derek T. Ahneman, Jesús G Estrada, Shishi Lin, S. Dreher, A. Doyle. Science, 2018

816 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Organic Chemistry

View Paper: https://www.semanticscholar.org/paper/a44f2ad10815051142c97fb466c51c8c9eda1d7f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Connor W. Coley
2. A. Pais
3. Yue Zhao
4. Jure Leskovec
5. Jimeng Sun
6. Tânia F Cova

In Organic Chemistry

1. Connor W. Coley
2. A. Pais
3. Jimeng Sun
4. Wenhao Gao
5. Tianfan Fu
6. Tânia F Cova

Citations in the corpus

• Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning (2019, positive): “A very recent example of the utility of machine learning methods in catalysis is the prediction of reaction yields by Doyle and co-workers (27, 28).”
• ARTIFICIAL INTELLIGENCE AND PERSONALIZED MEDICINE (2019, background): “[20 –22] AI has also been used in studies of very basic phenomena, such as particle physics, to probe how materials interact.”
• Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns (2019, methodology): “…temperature, and among others) for ensuring region/chemo/stereo selectivity and obtaining the highest yields, estimating the precise rate, yield and time for the reaction, predicting major/minor product, and also evaluating similarity between reactions (Wei et al., 2016; Ahneman et al., 2018).”