Self-optimizing Bayesian for continuous flow synthesis process†

IF 6.2 Q1 CHEMISTRY, MULTIDISCIPLINARY
Runzhe Liu, Zihao Wang, Wenbo Yang, Jinzhe Cao and Shengyang Tao
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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.

Abstract Image

用于连续流合成过程的自优化贝叶斯算法
人工智能(AI)与化学的融合推动了连续流合成技术的发展,促进了程序控制的自动流程优化。优化算法在自动优化过程中发挥着举足轻重的作用。算法准确性和预测能力的提高将进一步降低优化流程的相关成本。我们设计了一种自优化贝叶斯算法(SOBayesian),将高斯过程回归作为代理模型。自适应策略在模型训练过程中实施,而不是在获取函数时实施,以提高模型的建模效率。该算法有助于优化通过布赫瓦尔德-哈特维格反应合成吡啶基苯甲酰胺(一种重要的医药中间体)的连续流合成工艺。在不到 30 轮的迭代优化中,产量达到了 79.1%,在减少先验数据的情况下进行的后续优化成功减少了 27.6% 的实验次数,大大降低了实验成本。根据实验结果可以得出结论,该反应是受动力学控制的。这为类似反应的优化提供了思路,也为连续流自动优化提供了新的研究思路。
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CiteScore
2.80
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