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Development of Heterogeneous Enantioselective Catalysts using Chiral Metal-Organic Frameworks MOFs
Published on: January 17, 2020
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Connecting and Analyzing Enantioselective Bifunctional Hydrogen Bond Donor Catalysis Using Data Science Tools
Jacob Werth1, Matthew S Sigman1
1Department of Chemistry, University of Utah, 315 South 1400 East, Salt Lake City, Utah 84112, United States.
Journal of the American Chemical Society
|August 27, 2020
Summary
Data science methods connect catalyst and substrate structures in enantioselective bifunctional hydrogen bond donor (HBD) catalysis. This approach predicts outcomes, aiding future catalyst design and understanding noncovalent interactions in asymmetric organocatalysis.
Area of Science:
- Organic Chemistry
- Computational Chemistry
- Data Science
Background:
- Generalizing asymmetric organocatalysis is challenging due to complex noncovalent interactions.
- Empirical optimization of catalyst structure and conditions is common.
- Bifunctional hydrogen bond donor (HBD) catalysis has yielded diverse enantioselective transformations.
Purpose of the Study:
- To apply data science methods to correlate catalyst and substrate features in enantioselective bifunctional HBD catalysis.
- To analyze the noncovalent interactions driving asymmetric induction.
- To develop a predictive platform for future HBD catalysis applications.
Main Methods:
- Iterative statistical modeling using mechanism-specific computational parameters.
- Analysis based on proposed transition states.
- Extrapolation to out-of-sample examples for prediction.
Main Results:
- Established statistical models linking catalyst/substrate structures to enantioselectivity.
- Identified key noncovalent interactions responsible for asymmetric induction.
- Demonstrated predictive capability for new catalytic systems.
Conclusions:
- Data science offers a powerful, non-empirical approach to understanding and designing asymmetric organocatalysis.
- The developed models can guide the rational design of future bifunctional HBD catalysts.
- This workflow unifies various activation modes in asymmetric organocatalysis.
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