A substrate descriptor based approach for the prediction and understanding of the regioselectivity in caged catalyzed hydroformylation

The use of data driven tools to predict the selectivity of homogeneous catalysts has received considerable attention in the past years. In these studies often the catalyst structure is varied, but the use of substrate descriptors to rationalize the catalytic outcome is relatively unexplored. To study whether this may be an effective tool, we investigated both an encapsulated and a non-encapsulated rhodium based catalyst in the hydroformylation reaction of 41 terminal alkenes. For the non-encapsulated catalyst, CAT2, the regioselectivity of the acquired substrate scope could be predicted with high accuracy using the Δ(13)C NMR shift of the alkene carbon atoms as a descriptor (R(2) = 0.74) and when combined with a computed intensity of the C[double bond, length as m-dash]C stretch vibration (I(C[double bond, length as m-dash]C stretch)) the accuracy increased further (R(2) = 0.86). In contrast, a substrate descriptor approach with an encapsulated catalyst, CAT1, appeared more challenging indicating a confined space effect. We investigated Sterimol parameters of the substrates as well as computer-aided drug design descriptors of the substrates, but these parameters did not result in a predictive formula. The most accurate substrate descriptor based prediction was made with the Δ(13)C NMR shift and I(C[double bond, length as m-dash]C stretch) (R(2) = 0.52), suggestive of the involvement of CH–π interactions. To further understand the confined space effect of CAT1, we focused on the subset of 21 allylbenzene derivatives to investigate predictive parameters unique for this subset. These results showed the inclusion of a charge parameter of the aryl ring improved the regioselectivity predictions, which is in agreement with our assessment that noncovalent interactions between the phenyl ring of the cage and the aryl ring of the substrate are relevant for the regioselectivity outcome. However, the correlation is still weak (R(2) = 0.36) and as such we are investigating novel parameters that should improve the overall regioselectivity outcome.