Predicting reaction performance in C–N cross-coupling using machine learning
Abstract
A guide for catalyst choice in the forest: Chemists often discover reactions by applying catalysts to a series of simple compounds. Tweaking those reactions to tolerate more structural complexity in pharmaceutical research is time-consuming. Ahneman et al. report that machine learning can help. Using a high-throughput data set, they trained a random forest algorithm to predict which specific palladium catalysts would best tolerate isoxazoles (cyclic structures with an N–O bond) during C–N bond formation. The predictions also helped to guide analysis of the catalyst inhibition mechanism. Science , this issue p. 186
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One-shot models such as LASSO or random forests are widely used both as ML models for property prediction and for selection of important features.
“In a similar fashion, one-shot models such as least absolute shrinkage and selection operator (LASSO) or random forests are widely used both as ML models for property prediction (61) and for selection of important features (22,32,40,43) (Figure 4).”
From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [61]
As quantum chemical modeling became tractable, first-principles descriptors were used, with frontier orbital energies or partial charges playing an important role in building transferable models capable of predicting experimental catalyst activities.
“As quantum chemical modeling became tractable, first-principles descriptors were used, with frontier orbital energies or partial charges playing an important role in building transferable models capable of predicting experimental catalyst activities (59,61).”
From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [61]
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- Authors
- Ahneman, D. T., Estrada, J. G., Lin, S., Dreher, S. D., & Doyle, A. G.
- Affiliations
- Princeton University (US)
- Merck & Co., Inc., Rahway, NJ, USA (United States) (US)
- Corresponding author
- Dreher, Spencer D.
- Doyle, Abigail G.
- Venue
- Science
- Published
- 2018-04-13
- Volume
- 360
- Issue
- 6385
- Pages
- 186-190
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2785942661
- Cited by
- 1,058
- References
- 42
- Citations by year
- 2026: 112 · 2025: 160 · 2024: 154 · 2023: 157 · 2022: 124 · 2021: 130 · 2020: 100 · 2019: 89 · 2018: 32
- FWCI
- 32.76
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Chemistry › Organic Chemistry › Catalytic Cross-Coupling Reactions
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- C–N cross-coupling, Buchwald-Hartwig amination, palladium catalysis, aryl halides, 4-methylaniline, reaction yield prediction, random forest, high-throughput experimentation, molecular descriptors
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