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Jingxuan Ge

Showing results (1-10 of 21) with videos related to

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Ultramicroscopy|November 5, 2018
Measuring the areal density of nanomaterials by electron energy-loss spectroscopyMengkun Tian, Ondrej Dyck, Jingxuan Ge, et al.
Current Opinion in Structural Biology|September 11, 2025
Dynamic characteristics of proteolysis-targeting chimera systems revealed by in silico computationsKexin Xu, Jingxuan Ge, Rongfan Tang, et al.
Trends in Pharmacological Sciences|November 20, 2024
Development of PROTACs using computational approachesJingxuan Ge, Chang-Yu Hsieh, Meijing Fang, et al.
Chemical Communications (Cambridge, England)|February 2, 2017
[V<sub>4</sub>Mo<sub>3</sub>O<sub>14</sub>(NAr)<sub>3</sub>(μ<sub>2</sub>-NAr)<sub>3</sub>]<sup>2-</sup>: the first polyarylimido-stabilized molybdovanadate clusterYichao Huang, Jiangwei Zhang, Jingxuan Ge, et al.
Journal of Chemical Information and Modeling|July 23, 2025
Mechanistic Insights into the Binding of Different Antagonists to 5-HT1AR: A Molecular Docking and Molecular Dynamics Simulation StudyLulu Guan, Dushuo Feng, Jingxuan Ge, et al.
Frontiers in Microbiology|December 15, 2017
A Novel TetR Family Transcriptional Regulator, CalR3, Negatively Controls Calcimycin Biosynthesis in <i>Streptomyces chartreusis</i> NRRL 3882Lixia Gou, Tiesheng Han, Xiaoxia Wang, et al.
Journal of Chemical Information and Modeling|August 25, 2025
PepBAN: A Deep Learning Framework with Bilinear Attention and Adversarial Learning for Peptide-Protein Interaction PredictionShuaiyan Li, Xiaorui Wang, Yuchen Zhu, et al.
ACS Nano|July 29, 2014
Ferroplasmons: intense localized surface plasmons in metal-ferromagnetic nanoparticlesRitesh Sachan, Abhinav Malasi, Jingxuan Ge, et al.
Chemical Science|May 5, 2025
TRAP: a contrastive learning-enhanced framework for robust TCR-pMHC binding prediction with improved generalizabilityJingxuan Ge, Jike Wang, Qing Ye, et al.
Journal of Chemical Information and Modeling|August 30, 2022
Determination of Molecule Category of Ligands Targeting the Ligand-Binding Pocket of Nuclear Receptors with Structural Elucidation and Machine LearningQinghua Wang, Zhe Wang, Sheng Tian, et al.
Pageof 3

Showing results (1-10 of 21) with videos related to

Sort By:
Pageof 3
Ultramicroscopy|November 5, 2018
Measuring the areal density of nanomaterials by electron energy-loss spectroscopyMengkun Tian, Ondrej Dyck, Jingxuan Ge, et al.
Current Opinion in Structural Biology|September 11, 2025
Dynamic characteristics of proteolysis-targeting chimera systems revealed by in silico computationsKexin Xu, Jingxuan Ge, Rongfan Tang, et al.
Trends in Pharmacological Sciences|November 20, 2024
Development of PROTACs using computational approachesJingxuan Ge, Chang-Yu Hsieh, Meijing Fang, et al.
Chemical Communications (Cambridge, England)|February 2, 2017
[V<sub>4</sub>Mo<sub>3</sub>O<sub>14</sub>(NAr)<sub>3</sub>(μ<sub>2</sub>-NAr)<sub>3</sub>]<sup>2-</sup>: the first polyarylimido-stabilized molybdovanadate clusterYichao Huang, Jiangwei Zhang, Jingxuan Ge, et al.
Journal of Chemical Information and Modeling|July 23, 2025
Mechanistic Insights into the Binding of Different Antagonists to 5-HT1AR: A Molecular Docking and Molecular Dynamics Simulation StudyLulu Guan, Dushuo Feng, Jingxuan Ge, et al.
Frontiers in Microbiology|December 15, 2017
A Novel TetR Family Transcriptional Regulator, CalR3, Negatively Controls Calcimycin Biosynthesis in <i>Streptomyces chartreusis</i> NRRL 3882Lixia Gou, Tiesheng Han, Xiaoxia Wang, et al.
Journal of Chemical Information and Modeling|August 25, 2025
PepBAN: A Deep Learning Framework with Bilinear Attention and Adversarial Learning for Peptide-Protein Interaction PredictionShuaiyan Li, Xiaorui Wang, Yuchen Zhu, et al.
ACS Nano|July 29, 2014
Ferroplasmons: intense localized surface plasmons in metal-ferromagnetic nanoparticlesRitesh Sachan, Abhinav Malasi, Jingxuan Ge, et al.
Chemical Science|May 5, 2025
TRAP: a contrastive learning-enhanced framework for robust TCR-pMHC binding prediction with improved generalizabilityJingxuan Ge, Jike Wang, Qing Ye, et al.
Journal of Chemical Information and Modeling|August 30, 2022
Determination of Molecule Category of Ligands Targeting the Ligand-Binding Pocket of Nuclear Receptors with Structural Elucidation and Machine LearningQinghua Wang, Zhe Wang, Sheng Tian, et al.
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