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Optimización de la reacción bayesiana como herramienta para la síntesis química

Benjamin J Shields¹, Jason Stevens², Jun Li²

¹Department of Chemistry, Princeton University, Princeton, NJ, USA.

|February 4, 2021

Resumen

La optimización bayesiana mejora significativamente la optimización de la reacción química, superando la toma de decisiones humanas en eficiencia y consistencia. Este enfoque basado en datos mejora la síntesis de productos químicos funcionales en los laboratorios.

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Área de la Ciencia:

Química sintética
Inteligencia artificial
Química computacional

Sus antecedentes:

La reacción y la optimización de parámetros son cruciales en la química sintética y la inteligencia artificial.
Los altos costos experimentales exigen estrategias de optimización eficientes.
La optimización bayesiana sobresale en el aprendizaje automático, pero está poco explorada para las reacciones químicas.

Objetivo del estudio:

Desarrollar un marco y una herramienta de código abierto para la optimización de la reacción bayesiana.
Evaluar el rendimiento de la optimización bayesiana frente a la toma de decisiones humanas en química sintética.
Aplicar la optimización bayesiana a los desafíos de síntesis química en el mundo real.

Principales métodos:

Desarrolló un marco de optimización de reacción bayesiana y una herramienta de software.
Datos de referencia recogidos para una reacción de arilación directa catalizada por paladio.
Comparó la optimización bayesiana con expertos humanos a través de un juego en línea vinculado a experimentos de laboratorio.

Principales resultados:

La optimización bayesiana demostró una eficiencia de optimización promedio superior en comparación con la toma de decisiones humana.
La optimización bayesiana mostró una mayor consistencia en los resultados que los expertos humanos.
Se aplicó con éxito la optimización bayesiana a las reacciones de Mitsunobu y de desoxifluoración.

Conclusiones:

La optimización bayesiana es una herramienta poderosa para la optimización de reacciones químicas.
Este enfoque basado en datos mejora la eficiencia y la consistencia en el diseño experimental.
La adopción de la optimización bayesiana puede conducir a una síntesis más eficiente de productos químicos funcionales.