Leveraging Limited Experimental Data with Machine Learning: Differentiating a Methyl from an Ethyl Group in the Corey–Bakshi–Shibata Reduction

化学 催化作用 选择性 对映体过量 超分子化学 基质(水族馆) 图形 组合化学 机器学习 有机化学 对映选择合成 计算机科学 理论计算机科学 分子 海洋学 地质学
作者
Osvaldo José Ribeiro Pereira,Marcel Ruth,Dennis Gerbig,Raffael C. Wende,Peter R. Schreiner
出处
期刊:Journal of the American Chemical Society [American Chemical Society]
标识
DOI:10.1021/jacs.4c01286
摘要

We present a case study on how to improve an existing metal-free catalyst for a particularly difficult reaction, namely, the Corey–Bakshi–Shibata (CBS) reduction of butanone, which constitutes the classic and prototypical challenge of being able to differentiate a methyl from an ethyl group. As there are no known strategies on how to address this challenge, we leveraged the power of machine learning by constructing a realistic (for a typical laboratory) small, albeit high-quality, data set of about 100 reactions (run in triplicate) that we used to train a model in combination with a key-intermediate graph (of substrate and catalyst) to predict the differences in Gibbs activation energies ΔΔG‡ of the enantiomeric reaction paths. With the help of this model, we were able to select and subsequently screen a small selection of catalysts and increase the selectivity for the CBS reduction of butanone to 80% enantiomeric excess (ee), the highest possible value achieved to date for this substrate with a metal-free catalyst, thereby also exceeding the best available enzymatic systems (64% ee) and the selectivity with Corey's original catalyst (60% ee). This translates into a >50% improvement in relative ΔG‡ from 0.9 to 1.4 kcal mol–1. We underscore the transformative potential of machine learning in accelerating catalyst design because we rely on a manageable small data set and a key-intermediate graph representing a combination of catalyst and substrate graphs in lieu of a transition-state model. Our results highlight the synergy of synthetic chemistry and data-centric approaches and provide a blueprint for future catalyst optimization.
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