Self-optimizing Bayesian for continuous flow synthesis process

Abstract

The integration of artificial intelligence (AI) and chemistry has propelled the advancement of continuous flow synthesis, facilitating program-controlled automatic process optimization. Optimization algorithms play a pivotal role in the automated optimization process. The increased accuracy and predictive capability of the algorithms will further mitigate the costs associated with optimization processes. A self-optimizing Bayesian algorithm (SOBayesian), incorporating Gaussian process regression as a proxy model, has been devised. Adaptive strategies are implemented during the model training process, rather than on the acquisition function, to elevate the modeling efficacy of the model. This algorithm facilitated optimizing the continuous flow synthesis process of pyridinylbenzamide, an important pharmaceutical intermediate, via the Buchwald–Hartwig reaction. Achieving a yield of 79.1% in under 30 rounds of iterative optimization, subsequent optimization with reduced prior data resulted in a successful 27.6% reduction in the number of experiments, significantly lowering experimental costs. Based on the experimental results, it can be concluded that the reaction is kinetically controlled. It provides ideas for optimizing similar reactions and new research ideas in continuous flow automated optimization.