ArticleNature communications2024
In-silico-assisted derivatization of triarylboranes for the catalytic reductive functionalization of aniline-derived amino acids and peptides with H
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Triarylborane-Catalyzed Homogeneous Molecular Transformations Using Organic Reductants.Chemical reviews · 2026Article
- An Isolated LithiumJournal of the American Chemical Society · 2025Article
- Boosting Turnover in the Triarylborane-Catalyzed Hydrogenation ofPrecision chemistry · 2025Article
- Monodentate σ-Accepting Boron-Based Ligands Bearing Square-Planar Ni(0) Centers.Journal of the American Chemical Society · 2025Article
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Authors and funding
5 authors.
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Abstract
Cheminformatics-based machine learning (ML) has been employed to determine optimal reaction conditions, including catalyst structures, in the field of synthetic chemistry. However, such ML-focused strategies have remained largely unexplored in the context of catalytic molecular transformations using Lewis-acidic main-group elements, probably due to the absence of a candidate library and effective guidelines (parameters) for the prediction of the activity of main-group elements. Here, the construction of a triarylborane library and its application to an ML-assisted approach for the catalytic reductive alkylation of aniline-derived amino acids and C-terminal-protected peptides with aldehydes and H
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