Continuous flow process optimization aided by machine learning for a pharmaceutical intermediate

IF 2 4区 化学 Q3 CHEMISTRY, MULTIDISCIPLINARY
Jinlin Zhu, Chenyang Zhao, Li Sheng, Dadong Shen, Gang Fan, Xufeng Wu, Lushan Yu, Kui Du
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引用次数: 0

Abstract

In this paper, we demonstrate the use of machine learning to optimize the continuous flow process of a crucial intermediate in the production of Nemonoxacin. Our focus is to achieve the good yield and enantioselectivity in the construction of chiral methyl group utilize the initial 29 experimental datasets and consider six important variables. Employing Single-Objective Bayesian optimization (SOBO), we achieved an impressive predicted yield of up to 89.7%, which is consistent with the experimental results, with a yield of 89.5%. Additionally, A Multi-Objective Bayesian Optimization (MOBO) algorithm, namely qNEHVI, to strike a balance between yield and enantioselectivity in the continuous flow system is applied. The algorithm’s prediction, with a yield of 81.8% and enantioselectivity of 97.85%, was experimentally validated, yielding 83.8% and 97.2%, respectively. This study effectively demonstrates that Bayesian optimization is a powerful tool for optimizing the continuous process in the production of active pharmaceutical ingredients (APIs).

Abstract Image

利用机器学习优化制药中间体的连续流工艺
在本文中,我们展示了如何利用机器学习来优化奈莫沙星生产过程中一个关键中间体的连续流工艺。我们的重点是利用最初的 29 个实验数据集,并考虑六个重要变量,在构建手性甲基的过程中实现良好的产率和对映选择性。利用单目标贝叶斯优化(SOBO),我们获得了令人印象深刻的高达 89.7% 的预测产率,这与实验结果一致,产率为 89.5%。此外,我们还采用了一种多目标贝叶斯优化(MOBO)算法,即 qNEHVI,以在连续流系统中实现产率和对映体选择性之间的平衡。实验验证了该算法的预测结果,产率为 81.8%,对映体选择性为 97.85%,产率和对映体选择性分别为 83.8%和 97.2%。这项研究有效地证明了贝叶斯优化算法是优化活性药物成分 (API) 连续生产过程的有力工具。
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来源期刊
Journal of Flow Chemistry
Journal of Flow Chemistry CHEMISTRY, MULTIDISCIPLINARY-
CiteScore
6.40
自引率
3.70%
发文量
29
审稿时长
>12 weeks
期刊介绍: The main focus of the journal is flow chemistry in inorganic, organic, analytical and process chemistry in the academic research as well as in applied research and development in the pharmaceutical, agrochemical, fine-chemical, petro- chemical, fragrance industry.
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