Computational Mapping of Dirhodium(II) Catalysts

Abstract

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.

Keywords: computational chemistry; data-led prediction; homogeneous catalysis; ligands; rhodium.

Scheme 1

Dirhodium complexes modelled using standard DFT (BP86/6‐31G(d), with MWB28 on Rh).

Figure 1

The 48 ligands in the dirhodium(II) complexes that were used to generate the database. Ligands are drawn with coordinating atoms at the bottom. The labels correspond to the corresponding dirhodium(II) complexes bearing these ligands. Catalysts that are commercially available (circle) or that were investigated in high‐throughput experiments (square) are indicated.

Figure 2

Steric parameters captured. The He₈ ring was positioned 1.9 Å from the Rh atom forming the carbene bond. ^[17b] |wV| was aligned along the vector of the Rh‐Rh bond ^[22] (see Figure S2† for examples).

Figure 3

Calculated Pearson R correlation coefficient map for the 14 selected descriptors.

Figure 4

PCA score and loadings plots. Optimal solution for the PCA of dirhodium(II) catalysts capturing around 86 % total variance. PC1/PC2/PC3 explained variance: 54, 22 and 11 %. Mean squared error loss from projection: 0.142.

Figure 5

Effect of catalyst on the outcome of a reaction that underpinned the discovery of the androgen receptor agonist 6. Panel A: Reaction overview. Panel B: HPLC yield of the β‐lactam 6 as a function of catalyst and solvent shown within the context of the catalyst map (green, circles scaled according to yield, with darker shades also corresponding to higher % yield of 6); catalysts that were not investigated experimentally are also shown (grey, 4 z labelled). Panel C: Ratio of HPLC peak areas corresponding to the alternative products 6 and 7 (circles scaled according to conversion, with red favouring 6 and blue favouring 7) (see Figure 1 for details of catalysts and Figures S13–S17 for PC1/PC3 maps and correlations).