Z
Zhizheng Wang
Researcher at Central China Normal University
Publications - 12
Citations - 95
Zhizheng Wang is an academic researcher from Central China Normal University. The author has contributed to research in topics: Medicine & Biology. The author has an hindex of 1, co-authored 1 publications receiving 6 citations.
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Journal ArticleDOI
Construction of emissive ruthenium(II) metallacycle over 1000 nm wavelength for in vivo biomedical applications
TL;DR: Ru1085 as mentioned in this paper is a metal-based metallacycle with an excitation at 808 nm and emission over 1000 nm, which holds deep optical penetration (up to 6 mm) and enhanced chemo-phototherapy activity.
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Structural insights into the photoactivation of Arabidopsis CRY2
Ling Ma,Ling Ma,Ling Ma,Zeyuan Guan,Qiang Wang,Xuhui Yan,Jing Wang,Zhizheng Wang,Jianbo Cao,Delin Zhang,Xin Gong,Ping Yin +11 more
TL;DR: This study reports the cryogenic electron microscopy structure of a blue-light-activated CRY2 tetramer at a resolution of 3.1 Å, which shows how the CRY1 tetramer assembles and provides insights into blue- light-mediated activation ofCRY2 and a theoretical basis for developing regulators of CRYs for optogenetic manipulation.
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Exploring the kinase-inhibitor fragment interaction space facilitates the discovery of kinase inhibitor overcoming resistance by mutations
TL;DR: A comprehensive web platform KinaFrag is constructed for the fragment-based kinase inhibitor discovery to overcome resistance, and YT9 shows promising antiproliferative against tumor cells in vitro and effectively inhibits tumor growth in vivo for wild type TRK and TRK mutants.
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Discovery of Macrocycle-Based HPK1 Inhibitors for T-Cell-Based Immunotherapy.
Mingfang Wang,Zhizheng Wang,Zixi Li,Yi Gong,Cheng-Xiang Duan,Qian Hui Cheng,Wei Huang,Guang-Fu Yang +7 more
TL;DR: In this article , a series of macrocycle-based HPK1 inhibitors via a conformational constraint strategy was designed to achieve a higher selectivity to GLK, an HPK homology protein as a positive regulator of T-cell activation.
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Deciphering Nonbioavailable Substructures Improves the Bioavailability of Antidepressants by Serotonin Transporter.
Zhizheng Wang,Chao Yi,Jun-Jie Huang,Teng-Fei Xu,Kangzhi Chen,Zu-Sheng Wang,Ya-Ping Xue,Jielian Lu,Biao Nie,Ying Jun Zhang,Chuanfei Jin,Ge-Fei Hao +11 more
TL;DR: In this paper , a machine learning model was developed to identify non-bioavailable substructures based on their molecular properties and shows the accuracy of 83.5% in rats by replacing the non-available substructure of approved drug vilazodone.