Auto Machine Learning Assisted Preparation of Carboxylic Acid by TEMPO‐Catalyzed Primary Alcohol Oxidation. Issue 2 (5th December 2022)

• Record Type: Journal Article
• Authors: Qiu, Jia; Xu, Yougen; Su, Shimin; Gao, Yadong; Yu, Peiyuan; Ruan, Zhixiong; Liao, Kuangbiao
• Is Part Of: Chinese journal of chemistry. Volume 41:Issue 2(2023)
• Page Start: 143
• Page End: 150
• Publication Date: 2022-12-05
• Subjects: TEMPO -- Oxidation -- Primary alcohols -- Carboxylic acids -- AutoGluon; Chemistry -- Periodicals; 540.5
• DOI: 10.1002/cjoc.202200555
• Languages: English
• ISSNs: 1001-604X

Abstract

Comprehensive Summary: Though alcohol oxidations were considered as well‐established reactions, selecting productive conditions or predicting reaction yields for unseen alcohols remained as major challenges. Herein, an auto machine learning (ML) model for TEMPO‐catalyzed oxidation of primary alcohols to the corresponding carboxylic acids is disclosed. A dataset of 3444 data, consisting of 282 primary alcohols and 45 conditions, were generated using high‐throughput experimentation (HTE). With the HTE data and 105 descriptors, a multi‐label prediction was performed with AutoGluon (an open‐source auto machine learning framework) and KNIME (an open‐source data analytics platform). For the independent test of 240 reactions (a full matrix of 20 unseen alcohols and 12 conditions), AutoGluon with multi‐label prediction for yield prediction (AGMP) gave excellent performance. For external test of 1308 reactions (consisting of 84 alcohols and 45 conditions), AGMP still afforded good results with R 2 as 0.767 and MAE as 4.9%. The model also revealed that the newly generated descriptor (Y/N, classification of the reaction reactivity) was the most relevant descriptor for yield prediction, offering a new perspective to integrate HTE and ML in organic synthesis. Abstract : A machine learning model for TEMPO‐catalyzed alcohol oxidation was developed, superior prediction accuracy was achieved and unprecedented attention on multi‐label prediction of organic chemistry reactions was attracted.