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Contrasting Historical and Physical Perspectives in Asymmetric Catalysis: ΔΔG‡ versus Enantiomeric Excess

Our paper "Contrasting Historical and Physical Perspectives in Asymmetric Catalysis: ΔΔG‡ versus Enantiomeric Excess" explores advancements in modeling asymmetric catalysis through machine learning (ML). Traditionally, enantiomeric excess (ee) has been the primary metric for evaluating enantioselectivity. However, we argue for a shift towards using relative Gibbs free activation energies (ΔΔG‡), a parameter deeply rooted in transition-state theory and more physically representative of the catalytic process.

Analyzing ten diverse datasets, the study benchmarks ML models using both metrics. Results indicate that ΔΔG‡ offers superior generalizability, better incorporates temperature effects, and avoids the physical limitations inherent in ee-based models. Key advantages of ΔΔG‡ include more accurate predictions and the elimination of unphysical predictions like ee values exceeding 100%. Moreover, ΔΔG‡ better handles nonlinear transformations, resulting in improved statistical reliability.

This work provides practical guidelines for chemists, particularly those with experimental backgrounds, to integrate ML into catalyst design and optimization effectively. The authors emphasize that adopting ΔΔG‡ enhances the physical grounding of models, contributing to a deeper understanding of asymmetric catalysis and aiding in the pursuit of highly selective catalysts.

Publication: M. Ruth, T. Gensch, P. R. Schreiner, Angew. Chem. Int. Ed. 2024, 63, e202410308. https://doi.org/10.1002/anie.202410308

Dieses Bild wurde eingereicht von Prof. Dr. Peter R. Schreiner.

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