Chemistry-informed deep learning model for predicting stereoselectivity and absolute configuration in asymmetric hydrogenation.

Saved in:
Bibliographic Details
Title: Chemistry-informed deep learning model for predicting stereoselectivity and absolute configuration in asymmetric hydrogenation.
Authors: Cheng L; Guangdong Provincial Key Laboratory of Advanced Biomaterials, Shenzhen Intelligent Medical Engineering Laboratory, Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China.; Joint Key Laboratory of the Ministry of Education, Institute of Applied Physics and Materials Engineering, University of Macau, Macau, China., Shao PL; Guangzhou Municipal and Guangdong Provincial Key Laboratory of Molecular Target and Clinical Pharmacology, The NMPA and State Key Laboratory of Respiratory Disease, School of Pharmaceutical Sciences, Guangzhou Medical University, Guangzhou, China. shaopl@gzhmu.edu.cn., Lv J; Guangdong Provincial Key Laboratory of Advanced Biomaterials, Shenzhen Intelligent Medical Engineering Laboratory, Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China., Xiao H; Guangdong Provincial Key Laboratory of Advanced Biomaterials, Shenzhen Intelligent Medical Engineering Laboratory, Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China., Sun Y; Guangdong Provincial Key Laboratory of Advanced Biomaterials, Shenzhen Intelligent Medical Engineering Laboratory, Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China., Yang J; Guangdong Provincial Key Laboratory of Advanced Biomaterials, Shenzhen Intelligent Medical Engineering Laboratory, Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China., Xu Z; Guangdong Provincial Key Laboratory of Advanced Biomaterials, Shenzhen Intelligent Medical Engineering Laboratory, Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China., Lv M; Guangdong Provincial Key Laboratory of Advanced Biomaterials, Shenzhen Intelligent Medical Engineering Laboratory, Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China., Wang G; Guangdong Provincial Key Laboratory of Advanced Biomaterials, Shenzhen Intelligent Medical Engineering Laboratory, Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China., Zhao S; Guangdong Provincial Key Laboratory of Advanced Biomaterials, Shenzhen Intelligent Medical Engineering Laboratory, Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China., Li J; Guangzhou Municipal and Guangdong Provincial Key Laboratory of Molecular Target and Clinical Pharmacology, The NMPA and State Key Laboratory of Respiratory Disease, School of Pharmaceutical Sciences, Guangzhou Medical University, Guangzhou, China., Jin Z; Guangdong Provincial Key Laboratory of Advanced Biomaterials, Shenzhen Intelligent Medical Engineering Laboratory, Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China., Tan X; Guangdong Provincial Key Laboratory of Advanced Biomaterials, Shenzhen Intelligent Medical Engineering Laboratory, Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China., Xing G; Joint Key Laboratory of the Ministry of Education, Institute of Applied Physics and Materials Engineering, University of Macau, Macau, China. gcxing@um.edu.mo., Zhang B; Guangdong Provincial Key Laboratory of Advanced Biomaterials, Shenzhen Intelligent Medical Engineering Laboratory, Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China. zhangb3@sustech.edu.cn.
Source: Nature computational science [Nat Comput Sci] 2026 Feb; Vol. 6 (2), pp. 145-155. Date of Electronic Publication: 2025 Dec 05.
Publication Type: Journal Article
Journal Info: Publisher: Springer Nature Country of Publication: United States NLM ID: 101775476 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2662-8457 (Electronic) Linking ISSN: 26628457 NLM ISO Abbreviation: Nat Comput Sci Subsets: MEDLINE; PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2662-8457
DOI:10.1038/s43588-025-00920-8