Impact of Model Selection and Conformational Effects on the Descriptors for In Silico Screening Campaigns: A Case Study of Rh-Catalyzed Acrylate Hydrogenation

Data-driven catalyst design is a promising approach for addressing the challenges in identifying suitable catalysts for synthetic transformations. Models with descriptor calculations relying solely on the precatalyst structure are potentially generalizable but may overlook catalyst–substrate interactions. This study explores substrate-specific interactions in the context of Rh-catalyzed asymmetric hydrogenation to elucidate the impact of substrate inclusion on the catalyst structure and on the descriptors derived from it. We compare a catalyst–substrate complex with methyl 2-acetamidoacrylate as a model substrate with the generic precatalyst structure involving a placeholder substrate, norbornadiene, across 11 Rh-based catalysts with bidentate bisphosphine ligands. For these systems, a full conformer ensemble analysis reveals an intriguing finding: the rigid substrate induces conformational freedom in the ligand. This flexibility gives rise to a more diverse conformer landscape, showing a previously overlooked aspect of catalyst–substrate dynamics. Electronic descriptor variations particularly highlight differences between substrate-specific and precatalyst structures. This study suggests that generic precatalyst-like models may lack crucial insights into the conformational freedom of the catalyst. We speculate that such conformational freedom may be a more general phenomenon that can influence the development of generalizable predictive models of computational TM-based catalysis.