Transferrable selectivity profiles enable prediction in synergistic catalyst space.

Area of Science:

• Organic Chemistry
• Catalysis
• Computational Chemistry

Background:

• Multi-catalytic enantioselective transformations are crucial for chiral molecule synthesis.
• Optimizing multiple catalyst components presents a significant challenge, hindering widespread adoption.
• Limited experimental data in synergistic catalyst spaces impedes algorithmic acceleration of screening.

Purpose of the Study:

• To develop a data-driven strategy for predicting catalyst selectivity in multi-catalytic systems.
• To overcome the barrier of limited data by translating selectivity relationships from well-studied systems.
• To demonstrate the applicability of this approach across diverse catalytic modes.

Main Methods:

• Developing quantitative structure-function relationships (QSFRs) from existing enantioselectivity datasets.
• Translating selectivity information from single-catalyst systems to synergistic, multi-catalyst reaction spaces.
• Validating the approach through three case studies involving Brønsted acid, chiral anion, and secondary amine catalysis.

Main Results:

• Successfully translated selectivity relationships from abundant data in one catalyst system to synergistic catalyst spaces.
• Demonstrated the ability to predict and elucidate selectivity in reactions involving multiple catalysts.
• Validated the approach across different catalytic modes, including Brønsted acid, chiral anion, and secondary amine catalysis.

Conclusions:

• The data-driven strategy effectively predicts and elucidates selectivity in multi-catalytic enantioselective reactions.
• This approach overcomes data limitations by leveraging information from simpler catalytic systems.
• The general workflow shows broad applicability for developing new enantioselective, multi-catalytic processes in asymmetric catalysis.