School of Chemistry and Astbury Centre for Structural Molecular Biology, University of Leeds, Leeds, LS29JT, UK.
GlaxoSmithKline Medicines Research Centre, Stevenage, SG12NY, UK.
Chemistry. 2021 Feb 1;27(7):2402-2409. doi: 10.1002/chem.202003801. Epub 2021 Jan 12.
The chemistry of dirhodium(II) catalysts is highly diverse, and can enable the synthesis of many different molecular classes. A tool to aid in catalyst selection, independent of mechanism and reactivity, would therefore be highly desirable. Here, we describe the development of a database for dirhodium(II) catalysts that is based on the principal component analysis of DFT-calculated parameters capturing their steric and electronic properties. This database maps the relevant catalyst space, and may facilitate exploration of the reactivity landscape for any process catalysed by dirhodium(II) complexes. We have shown that one of the principal components of these catalysts correlates with the outcome (e.g. yield, selectivity) of a transformation used in a molecular discovery project. Furthermore, we envisage that this approach will assist the selection of more effective catalyst screening sets, and, hence, the data-led optimisation of a wide range of rhodium-catalysed transformations.
二价铑(II)催化剂的化学性质非常多样,可以实现许多不同分子类型的合成。因此,一种能够辅助选择催化剂的工具,而不依赖于反应机理和反应活性,将是非常理想的。在这里,我们描述了一种基于主成分分析(PCA)的二价铑(II)催化剂数据库的开发,该数据库基于 DFT 计算参数,这些参数可以捕捉它们的空间和电子性质。该数据库映射了相关的催化剂空间,并可能有助于探索任何由二价铑(II)配合物催化的反应的反应性景观。我们已经表明,这些催化剂的一个主要成分与一个分子发现项目中使用的转化的结果(例如产率、选择性)相关。此外,我们设想这种方法将有助于选择更有效的催化剂筛选集,从而实现广泛的铑催化转化的数据驱动优化。