After independence, as corresponding author:
19. Chonghuan Zhang, Qianghua Lin, Biwei Zhu, Haopeng Yang, Xiao Lian, Hao Deng, Jiajun Zheng & Kuangbiao Liao*. SynAsk: Unleashing the Power of Large Language Models in Organic Synthesis. Chem. Sci. 2024
https://pubs.rsc.org/en/content/articlelanding/2024/sc/d4sc04757e
This article is part of the themed collection: 2024 Chemical Science HOT Article Collection
18. An Lin, Jingyuan Liu, Yougen Xu, Haiting Wu, Yating Chen, Yan Zhang, Lebin Su*, Xiaodan Zhao & Kuangbiao Liao*. High-throughput experimentation and machine learning-promoted synthesis of α-phosphoryloxy ketones via Ru-catalyzed P (O) OH insertion reactions of sulfoxonium ylides. Sci. China Chem. 2024
https://doi.org/10.1007/s11426-024-2313-5
17. Xin Hong, Qi Yang, Kuangbiao Liao, Jianfeng Pei, Mao Chen, Fanyang Mo, Hua Lu, Wen-Bin Zhang, Haisen Zhou, Jiaxiao Chen, Lebin Su, Shuo-Qing Zhang, Siyuan Liu, Xu Huang, Yi-Zhou Sun, Yuxiang Wang, Zexi Zhang, Zhunzhun Yu, Sanzhong Luo, Xue-Feng Fu, Shu-Li You. AI for organic and polymer synthesis. Sci. China Chem. 2024, 67, 2461-2496.
https://link.springer.com/article/10.1007/s11426-024-2072-4
16. Xuefeng Jiang, Sanzhong Luo, Kuangbiao Liao, Shan Jiang, Jing Ma, Jun Jiang, Zhigang Shuai. Artificial intelligence and automation to power the future of chemistry. Cell Reports Physical Science 2024, 5, 102049.
https://www.cell.com/cell-reports-physical-science/fulltext/S2666-3864(24)00318-7
15. Yougen Xu, Yadong Gao, Lebin Su*, Haiting Wu, Hao Tian, Majian Zeng, Chunqiu Xu, Xinwei Zhu & Kuangbiao Liao*. High-Throughput Experimentation and Machine Learning-Assisted Optimization of Iridium-Catalyzed Cross-Dimerization of Sulfoxonium Ylides. Angew. Chem. Int. Ed. 2023, 62, e202313638.
https://onlinelibrary.wiley.com/doi/abs/10.1002/anie.202313638
14. Dianzhao Lin, Guichun Fang, Kuangbiao Liao* (2023). Synthesize in a Smart Way: A Brief Introduction to Intelligence and Automation in Organic Synthesis. In: Qu, C., Liu, H. (eds) Machine Learning in Molecular Sciences. Challenges and Advances in Computational Chemistry and Physics, vol 36. Springer, Cham. https://doi.org/10.1007/978-3-031-37196-7_8 [book chapter]
13. Baiqing Li, Shimin Su, Chan Zhu, Jie Lin, Xinyue Hu, Lebin Su, Zhunzhun Yu, Kuangbiao Liao* & Hongming Chen*. A deep learning framework for accurate reaction prediction and its application on high-throughput experimentation data. Journal of Cheminformatics, 2023, 15, 72
12. Zhunzhun Yu, Yaxian Kong, Baiqing Li, Shimin Su, Jianhang Rao, Yadong Gao, Tianyong Tu, Hongming Chen* & Kuangbiao Liao*. HTE and AI-Assisted Development of DHP Catalyzed Decarboxylative Selenation. Chem. Comm. 2023, 59, 2935-2938.
https://pubs.rsc.org/en/Content/ArticleLanding/2023/CC/D2CC06217H
11. Yougen Xu, Feixiao Ren, Lebin Su*, Zhaoping Xiong, Xinwei Zhu, Xinyuan Lin, Nan Qiao, Hao Tian, Changen Tian & Kuangbiao Liao*. HTE and machine learning-assisted development of iridium (I)-catalyzed selective O–H bond insertion reactions toward carboxymethyl ketones. Org. Chem. Front. 2023, 10, 1153-1159.
https://pubs.rsc.org/en/content/articlehtml/2023/qo/d2qo01954j
10. Guichun Fang, Dian-Zhao Lin & Kuangbiao Liao*. Synthetic Automations: A Revolution From "Stone Age" to Modern Era. Chin. J. Chem. 2023, 41, 1075—1079.
https://onlinelibrary.wiley.com/doi/abs/10.1002/cjoc.202200713
9. Jia Qiu, Yougen Xu, Shimin Su, Yadong Gao, Peiyuan Yu, Zhixiong Ruan* & Kuangbiao Liao*. Auto Machine Learning Assisted Preparation of Carboxylic Acid by TEMPO-Catalyzed Primary Alcohol Oxidation. Chin. J. Chem. 2023, 41, 143-150.
https://onlinelibrary.wiley.com/doi/abs/10.1002/cjoc.202200555
8. Jia Qiu#, Jiancong Xie#, Shimin Su, Yadong Gao, Han Meng, Yuedong Yang* & Kuangbiao Liao* Selective Functionalization of Hindered meta-C–H bond of o-Alkylaryl Ketones Promoted by Automation and Deep Learning. Chem 2022, 8, 3275-3287.
https://doi.org/10.1016/j.chempr.2022.08.015
Before independence：
7. Yannick T. Boni, Ryan C. Cammarota, Kuangbiao Liao, Matthew S. Sigman* & Huw M. L. Davies* Leveraging Regio- and Stereoselective C(sp3)−H Functionalization of Silyl Ethers to Train a Logistic Regression Classification Model for 3 Predicting Site-Selectivity Bias. J. Am. Chem. Soc. 2022, 144, 15549–15561.
https://pubs.acs.org/doi/abs/10.1021/jacs.2c04383
6. Huw M. L. Davies & Kuangbiao Liao. Dirhodium tetracarboxylates as catalysts for selective intermolecular C–H functionalization. Nature Reviews Chemistry 2019, 3, 347–360.
https://www.nature.com/articles/s41570-019-0099-x
5. Wenbin Liu, Zhi Ren, Aaron T. Bosse, Kuangbiao Liao, Elizabeth L. Goldstein,John Bacsa, Djamaladdin G. Musaev, Brian M. Stoltz, Huw M. L. Davies*. Catalyst-Controlled Selective Functionalization of Unactivated C–H Bonds in the Presence of Electronically Activated C–H Bonds. J. Am. Chem. Soc. 2018, 140, 12247–12255.
https://pubs.acs.org/doi/10.1021/jacs.8b07534
4. Kuangbiao Liao, Wenbin Liu, Zachary L. Niemeyer, Zhi Ren, John Bacsa, Djamaladdin G Musaev, Matthew S. Sigman, and Huw M. L. Davies. Site-Selective Carbene-Induced C–H Functionalization Catalyzed by Dirhodium Tetrakis(triarylcyclopropanecarboxylate) Complexes. ACS Catal. 2018, 8, 1, 678-682.
https://pubs.acs.org/doi/abs/10.1021/acscatal.7b03421
3. Kuangbiao Liao,Yun-Fang Yang,Yingzi Li,Jacob N. Sanders,K. N. Houk,Djamaladdin G. Musaev &Huw M. L. Davies. Design of catalysts for site-selective and enantioselective functionalization of non-activated primary C–H bonds. Nature Chemistry 2018, 10, 1048–1055.
https://www.nature.com/articles/s41557-018-0087-7
2. Kuangbiao Liao, Thomas C. Pickel, Vyacheslav Boyarskikh, John Bacsa, Djamaladdin G. Musaev & Huw M. L. Davies.Site-selective anstereoselective functionalization of non-activated tertiary C–H bonds. Nature 2017, 551, 609–613.
https://www.nature.com/articles/nature24641
1. Kuangbiao Liao, Solymar Negretti, Djamaladdin G. Musaev, John Bacsa & Huw M. L. Davies. Site-selective and stereoselective functionalization of unactivated C–H bonds. Nature 2016, 533, 230–234.
https://www.nature.com/articles/nature17651
