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Machine learning-assisted amidase-catalytic enantioselectivity prediction and rational design of variants for improving enantioselectivity.

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作者: Zi-Lin,Li [1] ; Shuxin,Pei [2] ; Ziying,Chen [2] ; Teng-Yu,Huang [1] ; Xu-Dong,Wang [3] ; Lin,Shen [4] ; Xuebo,Chen [5] ; Qi-Qiang,Wang [1] ; De-Xian,Wang [1] ; Yu-Fei,Ao [6]
作者单位: Beijing National Laboratory for Molecular Sciences, CAS Key Laboratory of Molecular Recognition and Function, Institute of Chemistry, Chinese Academy of Sciences, Beijing, China.;University of Chinese Academy of Sciences, Beijing, China. [1] Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education, College of Chemistry, Beijing Normal University, Beijing, China. [2] Beijing National Laboratory for Molecular Sciences, CAS Key Laboratory of Molecular Recognition and Function, Institute of Chemistry, Chinese Academy of Sciences, Beijing, China. [3] Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education, College of Chemistry, Beijing Normal University, Beijing, China. lshen@bnu.edu.cn.;Yantai-Jingshi Institute of Material Genome Engineering, Yantai, China. lshen@bnu.edu.cn. [4] Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education, College of Chemistry, Beijing Normal University, Beijing, China. xuebochen@bnu.edu.cn.;Yantai-Jingshi Institute of Material Genome Engineering, Yantai, China. xuebochen@bnu.edu.cn.;Shandong Laboratory of Yantai Advanced Materials and Green Manufacturing, Yantai, China. xuebochen@bnu.edu.cn. [5] Beijing National Laboratory for Molecular Sciences, CAS Key Laboratory of Molecular Recognition and Function, Institute of Chemistry, Chinese Academy of Sciences, Beijing, China. aoyufe@iccas.ac.cn.;University of Chinese Academy of Sciences, Beijing, China. aoyufe@iccas.ac.cn. [6]
DOI 10.1038/s41467-024-53048-0
PMID 39389964
发布时间 2024-10-27
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