Zobrazeno 1 - 10 of 30 pro vyhledávání: '"Shimin Su"'
1
Akademický článek
A deep learning framework for accurate reaction prediction and its application on high-throughput experimentation data
Autor: Baiqing Li, Shimin Su, Chan Zhu, Jie Lin, Xinyue Hu, Lebin Su, Zhunzhun Yu, Kuangbiao Liao, Hongming Chen
Publikováno v: Journal of Cheminformatics, Vol 15, Iss 1, Pp 1-12 (2023)
Abstract In recent years, it has been seen that artificial intelligence (AI) starts to bring revolutionary changes to chemical synthesis. However, the lack of suitable ways of representing chemical reactions and the scarceness of reaction data has li
Externí odkaz: https://doaj.org/article/c17156337f624270b7ae21f9d05248bd
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2
HTE- and AI-assisted development of DHP-catalyzed decarboxylative selenation
Autor: Zhunzhun Yu, Yaxian Kong, Baiqing Li, Shimin Su, Jianhang Rao, Yadong Gao, Tianyong Tu, Hongming Chen, Kuangbiao Liao
Publikováno v: Chemical Communications. 59:2935-2938
1,4-Dihydropyridine (DHP) derivatives play key roles in biology, but are rarely used as catalysts in synthesis.
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::7648348d3408ae39775b25d2e3a047dd
https://doi.org/10.1039/d2cc06217h
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5
GraphRXN: A Novel Representation for Reaction Prediction
Autor: Baiqing Li, Shimin Su, Chan Zhu, Jie Lin, Xinyue Hu, Lebin Su, Zhunzhun Yu, Kuangbiao Liao, Hongming Chen
In recent years, it has been seen that artificial intelligence (AI) starts to bring revolutionary changes to chemical synthesis. However, the lack of suitable ways of representing chemical reactions and the scarceness of reaction data has limited the
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::ef1927cef63c71ca8acb1855dde85d98
https://doi.org/10.21203/rs.3.rs-1665893/v1
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6
SyntaLinker: automatic fragment linking with deep conditional transformer neural networks
Autor: Chao Zhao, Shuangjia Zheng, Shimin Su, Hongming Chen, Jun Xu, Yuyao Yang
Publikováno v: Chemical Science
Linking fragments to generate a focused compound library for a specific drug target is one of the challenges in fragment-based drug design (FBDD). Hereby, we propose a new program named SyntaLinker, which is based on a syntactic pattern recognition a
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cfbb5398133f701a19549588a797faaa
https://doi.org/10.1039/d0sc03126g
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7
SyntaLinker: Automatic Fragment Linking with Deep Conditional Transformer Neural Networks
Autor: Yuyao Yang, Shuangjia Zheng, Shimin Su, Jun Xu, Hongming Chen
Fragment based drug design represents a promising drug discovery paradigm complimentary to the traditional HTS based lead generation strategy. How to link fragment structures to increase compound affinity is remaining a challenge task in this paradig
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9d0a29e9bcd4a592ba36375ac8f6eeba
https://doi.org/10.26434/chemrxiv.12271508.v5
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8
AutoLinker: Automatic Fragment Linking with Deep Conditional Transformer Neural Networks
Autor: Yuyao Yang, Shuangjia Zheng, Shimin Su, Jun Xu, Hongming Chen
Fragment based drug design represents a promising drug discovery paradigm complimentary to the traditional HTS based lead generation strategy. How to link fragment structures to increase compound affinity is remaining a challenge task in this paradig
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c6796f222a651f4aba9b84d46b0eb96a
https://doi.org/10.26434/chemrxiv.12271508.v2
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9
Predicting the Feasibility of Copper(I)-Catalyzed Alkyne-Azide Cycloaddition Reactions Using a Recurrent Neural Network with a Self-Attention Mechanism
Autor: Shimin Su, Shuangjia Zheng, Jun Xu, Hanlin Gan, Fenglong Gu, Chao Zhao, Yuyao Yang
Publikováno v: Journal of chemical information and modeling. 60(3)
The copper(I)-catalyzed alkyne-azide cycloaddition (CuAAC) reaction, a major click chemistry reaction, is widely employed in drug discovery and chemical biology. However, the success rate of the CuAAC reaction is not satisfactory as expected, and in
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ef349260588b10f4175c58c2fdbd4753
https://pubmed.ncbi.nlm.nih.gov/32013419
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10
Akademický článek
A deep learning framework for accurate reaction prediction and its application on high-throughput experimentation data.
Autor: Li, Baiqing1 (AUTHOR), Su, Shimin1 (AUTHOR), Zhu, Chan1 (AUTHOR), Lin, Jie1 (AUTHOR), Hu, Xinyue1 (AUTHOR), Su, Lebin1 (AUTHOR), Yu, Zhunzhun1 (AUTHOR), Liao, Kuangbiao1 (AUTHOR) liao_kuangbiao@gzlab.ac.cn, Chen, Hongming1 (AUTHOR) chen_hongming@gzlab.ac.cn
Publikováno v: Journal of Cheminformatics. 8/11/2023, Vol. 15 Issue 1, p1-12. 12p.
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