Mechanistically Guided Workflow for Relating Complex Reactive Site Topologies to Catalyst Performance in C-H...

Area of Science:

• Catalysis and Reaction Engineering
• Computational Chemistry and Molecular Design
• Organic Synthesis and Methodology

Background:

• Altering substrate site-selectivity in C-H functionalization relies on catalyst scaffold design, mimicking enzyme active sites.
• Predicting reaction outcomes from catalyst 3D shape is challenging due to a lack of quantitative descriptors for complex reactive sites.
• Machine learning applications require numerical inputs to describe catalyst topology for predictive modeling.

Purpose of the Study:

• To introduce novel molecular descriptors, Spatial Molding for Approachable Rigid Targets (SMART), for quantifying catalyst reactive site constraints.
• To apply SMART descriptors to predict site-selectivity in C-H functionalization reactions using dirhodium catalysts.
• To develop predictive models for C-H functionalization site-selectivity based on catalyst steric congestion and electrophilicity.

Main Methods:

• Development and application of SMART descriptors to quantify spatial constraints of dirhodium catalyst reactive sites.
• C-H functionalization of 1-bromo-4-pentylbenzene using donor/acceptor carbene intermediates with an expansive library of dirhodium catalysts.
• Multivariate linear regression analysis to build site-selectivity models incorporating steric and electronic factors.

Main Results:

• Optimal terminal methylene site-selectivity achieved with Rh₂(S-2-Cl-5-MesTPCP)₄ (30.9:1 rr, 14:1 dr, 87% ee).
• Increased preference for benzylic C-H functionalization observed with catalysts lacking ortho-Cl substituents (Rh₂(TPCP)₄, Rh₂(S-PTAD)₄, Rh₂(S-TCPTAD)₄).
• Development of intuitive global site-selectivity models for 25 dirhodium catalysts, correlating steric congestion and electrophilicity with reaction outcomes.

Conclusions:

• SMART descriptors provide a quantitative method to describe catalyst topology, facilitating machine learning-based prediction of C-H functionalization site-selectivity.
• The study establishes a workflow for correlating ground-state catalyst models with transition states, applicable to diverse chemical and biological systems.
• This approach enables the design of catalysts with tailored reactivity and selectivity by delineating salient reactive site features.