Auto Machine Learning Assisted Preparation of Carboxylic Acid by TEMPO-Catalyzed Primary Alcohol Oxidation
Jia Qiu
Guangdong Laboratory Animals Monitoring Institute, Guangdong Provincial Key Laboratory of Laboratory Animals, Guangzhou, Guangdong, 510663 China
Guangzhou Municipal and Guangdong Provincial Key Laboratory of Molecular Target & Clinical Pharmacology, the NMPA and State Key Laboratory of Respiratory Disease, School of Pharmaceutical Sciences and the Fifth Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, 511436 China
Bioland Laboratory, Guangzhou, Guangdong, 510005 China
Search for more papers by this authorYougen Xu
Bioland Laboratory, Guangzhou, Guangdong, 510005 China
Search for more papers by this authorShimin Su
Bioland Laboratory, Guangzhou, Guangdong, 510005 China
Search for more papers by this authorYadong Gao
Bioland Laboratory, Guangzhou, Guangdong, 510005 China
Search for more papers by this authorPeiyuan Yu
Department of Chemistry, Southern University of Science and Technology, Shenzhen, Guangdong, 518055 China
Search for more papers by this authorCorresponding Author
Zhixiong Ruan
Guangzhou Municipal and Guangdong Provincial Key Laboratory of Molecular Target & Clinical Pharmacology, the NMPA and State Key Laboratory of Respiratory Disease, School of Pharmaceutical Sciences and the Fifth Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, 511436 China
E-mail: [email protected]; [email protected]Search for more papers by this authorCorresponding Author
Kuangbiao Liao
Guangdong Laboratory Animals Monitoring Institute, Guangdong Provincial Key Laboratory of Laboratory Animals, Guangzhou, Guangdong, 510663 China
Bioland Laboratory, Guangzhou, Guangdong, 510005 China
Guangzhou Laboratory, Guangzhou, Guangdong, 510320 China
E-mail: [email protected]; [email protected]Search for more papers by this authorJia Qiu
Guangdong Laboratory Animals Monitoring Institute, Guangdong Provincial Key Laboratory of Laboratory Animals, Guangzhou, Guangdong, 510663 China
Guangzhou Municipal and Guangdong Provincial Key Laboratory of Molecular Target & Clinical Pharmacology, the NMPA and State Key Laboratory of Respiratory Disease, School of Pharmaceutical Sciences and the Fifth Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, 511436 China
Bioland Laboratory, Guangzhou, Guangdong, 510005 China
Search for more papers by this authorYougen Xu
Bioland Laboratory, Guangzhou, Guangdong, 510005 China
Search for more papers by this authorShimin Su
Bioland Laboratory, Guangzhou, Guangdong, 510005 China
Search for more papers by this authorYadong Gao
Bioland Laboratory, Guangzhou, Guangdong, 510005 China
Search for more papers by this authorPeiyuan Yu
Department of Chemistry, Southern University of Science and Technology, Shenzhen, Guangdong, 518055 China
Search for more papers by this authorCorresponding Author
Zhixiong Ruan
Guangzhou Municipal and Guangdong Provincial Key Laboratory of Molecular Target & Clinical Pharmacology, the NMPA and State Key Laboratory of Respiratory Disease, School of Pharmaceutical Sciences and the Fifth Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, 511436 China
E-mail: [email protected]; [email protected]Search for more papers by this authorCorresponding Author
Kuangbiao Liao
Guangdong Laboratory Animals Monitoring Institute, Guangdong Provincial Key Laboratory of Laboratory Animals, Guangzhou, Guangdong, 510663 China
Bioland Laboratory, Guangzhou, Guangdong, 510005 China
Guangzhou Laboratory, Guangzhou, Guangdong, 510320 China
E-mail: [email protected]; [email protected]Search for more papers by this authorComprehensive 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 R2 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.
Supporting Information
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Appendix S1: Supporting Information |
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