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RESEARCH PAPER · Angewandte Chemie · 2020

Predicting Regioselectivity in Radical C−H Functionalization of Heterocycles through Machine Learning

Xin Li, Shuo‐Qing Zhang, Li‐Cheng Xu, Xin Hong

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42 direct citing works

Predicting Regioselectivity in Radical C−H Functionalization of Heterocycles through Machine LearningOriginalOrganic Superbases in Recent Synthetic Methodology Research116Scaffold‐Directed Face Selectivity Machine‐Learned from Vectors of Non‐covalent Interactions42Scaffold‐Directed Face Selectivity Machine‐Learned from Vectors of Non‐covalent Interactions15Towards Data‐Driven Design of Asymmetric Hydrogenation of Olefins: Database and Hierarchical Learning78Towards Data‐Driven Design of Asymmetric Hydrogenation of Olefins: Database and Hierarchical Learning6Emerging Building Blocks for Medicinal Chemistry: Recent Synthetic Advances126Is Organic Chemistry Really Growing Exponentially?24Is Organic Chemistry Really Growing Exponentially?6Study of the Relationship between Synthesis Descriptors and the Type of Zeolite Phase Formed in ZSM‐43 Synthesis by Using Machine Learning6Machine learning activation energies of chemical reactions83Machine intelligence for chemical reaction space124An Ensemble Structure and Physicochemical (SPOC) Descriptor for Machine‐Learning Prediction of Chemical Reaction and Molecular Properties35ARMer: A Python Library for Adaptive Resource Allocation in High-Throughput Workflow0Leveraging Regio- and Stereoselective C(sp 3 )–H Functionalization of Silyl Ethers to Train a Logistic Regression Classification Model for Predicting Site-Selectivity Bias33Reaxtica: A knowledge-guided machine learning platform for fast and accurate reaction selectivity and yield prediction0Reaxtica: A knowledge-guided machine learning platform for fast and accurate reaction selectivity and yield prediction1Bridging Chemical Knowledge and Machine Learning for Performance Prediction of Organic Synthesis51A Hybrid Machine-Learning Approach to Predict the Iridium-Catalyzed Borylation of C–H Bonds6Enhancing the nucleophilicity of benzylic boronates enables the divergent formation of C−C and C−X bonds0Data Generation for Machine Learning Interatomic Potentials and Beyond176The Importance of Atomic Charges for Predicting Site-Selective Ir-, Ru-, and Rh-Catalyzed C–H Borylations6Mechanism-informed machine learning for rational catalyst design: application to regioselectivity of allyl acetate hydroformylation0Stufenweises diversitätsbeschränktes maschinelles Lernen für die Optimierung hochdimensionaler Reaktionsbedingungen0Structure–Selectivity Relationship Study of IDPi Using Fragment and Remote Site Descriptors0

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  1. Organic Superbases in Recent Synthetic Methodology Research

    Chemistry - A European Journal · 2020-08-26

    Thomas R. Puleo, Stephen J. Sujansky, Shawn E. Wright, Jeffrey S. Bandar

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  2. Scaffold‐Directed Face Selectivity Machine‐Learned from Vectors of Non‐covalent Interactions

    Angewandte Chemie International Edition · 2021-04-19

    Martyna Moskal, Wiktor Beker, Sara Szymkuć, Bartosz A. Grzybowski

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  3. Scaffold‐Directed Face Selectivity Machine‐Learned from Vectors of Non‐covalent Interactions

    Angewandte Chemie · 2021-04-20

    Martyna Moskal, Wiktor Beker, Sara Szymkuć, Bartosz A. Grzybowski

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  4. Towards Data‐Driven Design of Asymmetric Hydrogenation of Olefins: Database and Hierarchical Learning

    Angewandte Chemie International Edition · 2021-08-09

    Li‐Cheng Xu, Shuo‐Qing Zhang, Xin Li, Miao‐Jiong Tang, Pei‐Pei Xie, Xin Hong

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  5. Towards Data‐Driven Design of Asymmetric Hydrogenation of Olefins: Database and Hierarchical Learning

    Angewandte Chemie · 2021-08-09

    Li‐Cheng Xu, Shuo‐Qing Zhang, Xin Li, Miao‐Jiong Tang, Pei‐Pei Xie, Xin Hong

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  6. Emerging Building Blocks for Medicinal Chemistry: Recent Synthetic Advances

    European Journal of Organic Chemistry · 2021-09-08

    Oleksandr O. Grygorenko, Dmitriy M. Volochnyuk, Bohdan V. Vashchenko

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  7. Is Organic Chemistry Really Growing Exponentially?

    Angewandte Chemie International Edition · 2021-09-24

    Sara Szymkuć, Tomasz Badowski, Bartosz A. Grzybowski

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  8. Is Organic Chemistry Really Growing Exponentially?

    Angewandte Chemie · 2021-09-24

    Sara Szymkuć, Tomasz Badowski, Bartosz A. Grzybowski

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  9. Study of the Relationship between Synthesis Descriptors and the Type of Zeolite Phase Formed in ZSM‐43 Synthesis by Using Machine Learning

    ChemistrySelect · 2021-10-18

    Ganesan Raman

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  10. Machine learning activation energies of chemical reactions

    Wiley Interdisciplinary Reviews Computational Molecular Science · 2021-12-30

    Toby Lewis‐Atwell, Piers A. Townsend, Matthew N. Grayson

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  11. Machine intelligence for chemical reaction space

    Wiley Interdisciplinary Reviews Computational Molecular Science · 2022-03-07

    Philippe Schwaller, Alain C. Vaucher, Rubén Laplaza, Charlotte Bunne, Andreas Krause, Clémence Corminbœuf, Teodoro Laino

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  12. An Ensemble Structure and Physicochemical (SPOC) Descriptor for Machine‐Learning Prediction of Chemical Reaction and Molecular Properties

    ChemPhysChem · 2022-04-27

    Qi Yang, Yidi Liu, Junjie Cheng, Yao Li, Siyuan Liu, Yingdong Duan, Long Zhang, Sanzhong Luo

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  13. ARMer: A Python Library for Adaptive Resource Allocation in High-Throughput Workflow

    ChemRxiv · 2022-06-30

    Qianzhen Shao, Zhongyue John Yang

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  14. Leveraging Regio- and Stereoselective C(sp 3 )–H Functionalization of Silyl Ethers to Train a Logistic Regression Classification Model for Predicting Site-Selectivity Bias

    Journal of the American Chemical Society · 2022-08-17

    Yannick T. Boni, Ryan C. Cammarota, Kuangbiao Liao, Matthew S. Sigman, Huw M. L. Davies

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  15. Reaxtica: A knowledge-guided machine learning platform for fast and accurate reaction selectivity and yield prediction

    ChemRxiv · 2022-08-21

    Kangjie Lin, Junren Li, Haoyu Lin, Jianfeng Pei, Luhua Lai

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  16. Reaxtica: A knowledge-guided machine learning platform for fast and accurate reaction selectivity and yield prediction

    ChemRxiv · 2022-08-30

    Kangjie Lin, Junren Li, Haoyu Lin, Jianfeng Pei, Luhua Lai

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  17. Bridging Chemical Knowledge and Machine Learning for Performance Prediction of Organic Synthesis

    Chemistry - A European Journal · 2022-10-07

    Shuo‐Qing Zhang, Li‐Cheng Xu, Shu‐Wen Li, João C. A. Oliveira, Xin Li, Lutz Ackermann, Xin Hong

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  18. A Hybrid Machine-Learning Approach to Predict the Iridium-Catalyzed Borylation of C–H Bonds

    ChemRxiv · 2022-11-03

    Eike Caldeweyher, Masha Elkin, Golsa Gheibi, Magnus J. Johansson, Christian Sköld, Per‐Ola Norrby, John F. Hartwig

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  19. Enhancing the nucleophilicity of benzylic boronates enables the divergent formation of C−C and C−X bonds

    ChemRxiv · 2023-04-12

    Liuzhou Gao, Xinyi Liang, Linke He, Guoao Li, Shengda Chen, Jia Cao, Jing Ma, Guoqiang Wang, Shuhua Li

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  20. Data Generation for Machine Learning Interatomic Potentials and Beyond

    Chemical Reviews · 2024-11-21

    Maksim Kulichenko, Benjamin T. Nebgen, Nicholas Lubbers, Justin S. Smith, Kipton M. Barros, Alice E. A. Allen, Adela Habib, Emily Shinkle, Nikita S. Fedik, Ying Wai Li, Richard A. Messerly, Sergei Tretiak

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  21. The Importance of Atomic Charges for Predicting Site-Selective Ir-, Ru-, and Rh-Catalyzed C–H Borylations

    The Journal of Organic Chemistry · 2025-04-23

    Shannon M. Stephens, Kyle M. Lambert

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  22. Mechanism-informed machine learning for rational catalyst design: application to regioselectivity of allyl acetate hydroformylation

    Catalysis Science & Technology · 2025-12-23

    Minhua Zhang, Yuzhe Sun, Yifei Chen, Lingtao Wang, Kai Song, Hao Gong

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  23. Stufenweises diversitätsbeschränktes maschinelles Lernen für die Optimierung hochdimensionaler Reaktionsbedingungen

    Angewandte Chemie · 2026-02-15

    Shu‐Wen Li, Shan Chen, João C. A. Oliveira, Shimin Zhang, Lutz Ackermann, Xin Hong

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  24. Structure–Selectivity Relationship Study of IDPi Using Fragment and Remote Site Descriptors

    Angewandte Chemie · 2026-05-13

    Yu Zhou, Roberta Properzi, Xiaozhou Wang, T Wang, Xingyu Wang, Hui Zhou, Benjamin List, G L F Cheng

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