Zobrazeno 1 - 7
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pro vyhledávání: '"Zeng, Kaipeng"'
Organic synthesis stands as a cornerstone of chemical industry. The development of robust machine learning models to support tasks associated with organic reactions is of significant interest. However, current methods rely on hand-crafted features or
Externí odkaz:
http://arxiv.org/abs/2411.17629
Autor:
Zhang, Yu, Yu, Ruijie, Zeng, Kaipeng, Li, Ding, Zhu, Feng, Yang, Xiaokang, Jin, Yaohui, Xu, Yanyan
High-throughput reaction condition (RC) screening is fundamental to chemical synthesis. However, current RC screening suffers from laborious and costly trial-and-error workflows. Traditional computer-aided synthesis planning (CASP) tools fail to find
Externí odkaz:
http://arxiv.org/abs/2407.15141
Autor:
Zeng, Kaipeng, yang, Bo, Zhao, Xin, Zhang, Yu, Nie, Fan, Yang, Xiaokang, Jin, Yaohui, Xu, Yanyan
Motivation: Retrosynthesis planning poses a formidable challenge in the organic chemical industry. Single-step retrosynthesis prediction, a crucial step in the planning process, has witnessed a surge in interest in recent years due to advancements in
Externí odkaz:
http://arxiv.org/abs/2404.00044
Autor:
Yang, Nianzu, Zeng, Kaipeng, Lu, Haotian, Wu, Yexin, Yuan, Zexin, Chen, Danni, Jiang, Shengdian, Wu, Jiaxiang, Wang, Yimin, Yan, Junchi
Neuronal morphology is essential for studying brain functioning and understanding neurodegenerative disorders. As acquiring real-world morphology data is expensive, computational approaches for morphology generation have been studied. Traditional met
Externí odkaz:
http://arxiv.org/abs/2401.09500
Graph diffusion equations are intimately related to graph neural networks (GNNs) and have recently attracted attention as a principled framework for analyzing GNN dynamics, formalizing their expressive power, and justifying architectural choices. One
Externí odkaz:
http://arxiv.org/abs/2310.06417
Machine learning, particularly graph learning, is gaining increasing recognition for its transformative impact across various fields. One such promising application is in the realm of molecule design and discovery, notably within the pharmaceutical i
Externí odkaz:
http://arxiv.org/abs/2202.09212
Autor:
Zeng K; MoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, 200240, Shanghai, China., Yang B; Frontiers Science Center for Transformative Molecules (FSCTM), Zhangjiang Institute for Advanced Study, Shanghai Jiao Tong University, Shanghai, 200240, Shanghai, China., Zhao X; MoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, 200240, Shanghai, China., Zhang Y; MoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, 200240, Shanghai, China., Nie F; Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, Shanghai, China., Yang X; MoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, 200240, Shanghai, China., Jin Y; MoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, 200240, Shanghai, China. jinyh@sjtu.edu.cn., Xu Y; MoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, 200240, Shanghai, China. yanyanxu@sjtu.edu.cn.
Publikováno v:
Journal of cheminformatics [J Cheminform] 2024 Jul 15; Vol. 16 (1), pp. 80. Date of Electronic Publication: 2024 Jul 15.
