Data Science Meets Physical Organic Chemistry.

Area of Science:

• Synthetic Chemistry
• Catalysis
• Data Science

Background:

• Predictable catalyst design is a major goal in synthetic chemistry, particularly for asymmetric catalysis.
• Current methods often rely on empiricism and intuition due to the complexity of reaction mechanisms and catalyst performance.
• Enantioselectivity provides a rich platform to understand catalyst-structure-performance relationships.

Purpose of the Study:

• To develop data science-driven tools to quantitatively link reaction component attributes to enantioselectivity.
• To expand the power of linear free energy relationships (LFERs) for complex asymmetric catalysis.
• To harness data for improved asymmetric catalyst design and mechanistic hypothesis generation.

Main Methods:

• Developed a workflow integrating computational features of reaction components with enantioselectivity data.
• Utilized statistical modeling to relate molecular features (substrate, catalyst, transition states) to reaction outcomes.
• Applied data science tools to analyze complex patterns in reaction responses.

Main Results:

• Established a quantitative connection between molecular attributes and enantioselectivity.
• Demonstrated the utility of data science for predicting catalyst performance.
• Generated mechanistic hypotheses embedded within complex reaction data patterns.

Conclusions:

• Merging physical organic experiments with statistical modeling creates a feedback loop for mechanistic evaluation and catalyst design.
• This approach enables quantitative guidance for optimizing asymmetric reactions.
• Highlights the application of this workflow using chiral phosphoric acid catalysts (CPAs).