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Machine-leaning Assisted Optimization for Organo-catalytic Reactions in Flow System

Hettiarachchige Dona, Piyumi Wathsala 大阪大学 DOI:10.18910/85331

2021.09.24

概要

Optimization of reaction conditions is a crucial and unavoidable process in synthetic organic chemistry. In particular, developing a new synthetic reaction requires tedious screening and optimization of the relevant parameters, resulting in a wastage of a time, chemicals and energy. Thus a rapid, economical and effective protocol has been highly demanded. Herein, Machine Learning (ML) assisted optimization of multi-parameter screening for organoctalytic reactions1 in flow system has established by utilizing Gaussian Process Regression (GPR) and Bayesian Optimization (BO)2 with a minimum number of experimental studies.

Chapter 1. Exploration of flow reaction conditions using GPR for organocatalyzed domino reaction (Eq. 1)
The GPR was successfully applied to multi-parameter flow reaction screening for organocatalyzed Rauhut-Currier/[3+2] annulation sequence.3 After a brief experimental screening, the GPR with the experimental data (up to 10 entries) could rapidly predict the appropriate conditions (flow rate, temperature and substrate amount), leading to the formation of functionalized chiral spirooxindole analogues in high yields with up to 98% ee as a single diastereomer

Chapter 2. Bayesian optimization-assisted multi-parameter screening for an organo-catalytic flow synthesis of highly functionalized biaryls (Eq. 2)
The BO and experimental assessment using positive and negative results were successfully combined to accomplish the
simultaneous multi-parameter screening (including numerical and categorical parameters) for the preparation of highly functionalized biaryls. The BO with the experimental data (up to 15 entries) could rapidly predict the six optimal parameters (concentration, amount of reagent, flow rate, catalyst loading, temperature and type of flow reactor), resulting in biaryl with up to 96% yield.

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