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Development of Heterogeneous Enantioselective Catalysts using Chiral Metal-Organic Frameworks MOFs

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Published on: January 17, 2020

Conexión y análisis de la catálisis de donantes de enantioselectivos de enlaces de hidrógeno bifuncionales utilizando

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

Resumen

Los métodos de ciencia de datos conectan las estructuras del catalizador y el sustrato en la catálisis de donantes de enlaces de hidrógeno bifuncionales enantioselectivos (HBD). Este enfoque predice los resultados, ayudando al diseño futuro del catalizador y a comprender las interacciones no covalentes en la organocatálisis asimétrica.

Área de la Ciencia:

Química orgánica
Química computacional
Ciencia de los datos

Sus antecedentes:

La generalización de la organocatálisis asimétrica es un desafío debido a las complejas interacciones no covalentes.
La optimización empírica de la estructura y las condiciones del catalizador es común.
La catálisis del donante de enlace de hidrógeno bifuncional (HBD) ha producido diversas transformaciones enantioselectivas.

Objetivo del estudio:

Aplicar métodos científicos de datos para correlacionar las características del catalizador y el sustrato en la catálisis de HBD bifuncional enantioselectiva.
Para analizar las interacciones no covalentes que impulsan la inducción asimétrica.
Desarrollar una plataforma predictiva para futuras aplicaciones de catálisis de HBD.

Principales métodos:

Modelado estadístico iterativo utilizando parámetros computacionales específicos del mecanismo.
Análisis basado en los estados de transición propuestos.
Extrapolación a ejemplos fuera de la muestra para la predicción.

Principales resultados:

Modelos estadísticos establecidos que vinculan las estructuras catalizador/sustrato con la enantioselectividad.
Se identificaron las principales interacciones no covalentes responsables de la inducción asimétrica.
Capacidad predictiva demostrada para nuevos sistemas catalíticos.

Conclusiones:

La ciencia de datos ofrece un enfoque poderoso y no empírico para comprender y diseñar la organocatálisis asimétrica.
Los modelos desarrollados pueden guiar el diseño racional de futuros catalizadores de HBD bifuncionales.
Este flujo de trabajo unifica varios modos de activación en la organocatálisis asimétrica.

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