Quantitative Structure-Selectivity Relationships in Enantioselective Catalysis: Past, Present, and Future

Area of Science:

• Computational Chemistry
• Organic Chemistry
• Chemical Informatics

Background:

• The 21st century has seen increased use of computational methods in catalyst design.
• Chemoinformatics, applying informatics to chemistry, is crucial for predicting reaction activity and mechanisms.
• Advances in machine learning and computing power fuel this field.

Purpose of the Study:

• To review strategies for using quantitative structure-selectivity relationships (QSSR) in asymmetric catalysis.
• To organize QSSR methods by increasing molecular representation complexity.
• To showcase the application of QSSR in enantioselective transformations.

Main Methods:

• Introduction to basic QSSR features.
• Description of local parametrization and linear free energy relationships (LFERs).
• Explanation of global parametrization methods: continuous chirality measures (CCM), chirality codes, and molecular interaction fields (MIFs).

Main Results:

• QSSR strategies are categorized by molecular representation complexity.
• Local and global parametrization methods are detailed with examples.
• The review presents a comprehensive collection of QSSR applications in enantioselective catalysis.

Conclusions:

• Chemoinformatics, particularly QSSR, is a powerful tool for catalyst design.
• Combining computational and experimental approaches accelerates the discovery and optimization of catalysts and reactions.
• This field promises to revolutionize chemical research and development.