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Kaixian Chen

Researcher at Chinese Academy of Sciences

Publications -  403
Citations -  11476

Kaixian Chen is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Virtual screening & Chemistry. The author has an hindex of 47, co-authored 380 publications receiving 9209 citations. Previous affiliations of Kaixian Chen include Shanghai University & East China University of Science and Technology.

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Diversified strategy for the synthesis of DNA-encoded oxindole libraries.

TL;DR: The development of a series of novel on-DNA transformations based on oxindole scaffolds for the design and synthesis of diversity-oriented DNA-encoded libraries for screening are reported.
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QSAR analyses on avian influenza virus neuraminidase inhibitors using CoMFA, CoMSIA, and HQSAR

TL;DR: Investigation of the quantitative structure–activity relationship for 126 NA inhibitors with great structural diversities and wide range of bioactivities against influenza A virus shows clearly how steric, electrostatic, hydrophobicity, and individual fragments affect the potency of NA inhibitors.
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Advances in the study of berberine and its derivatives

TL;DR: This paper is a systematic review of the research advances of berberine and its derevatives in clinical application, pharmacodynamic mechanisms, molecular pharmacology, absorption and metabolism, and SAR studies to demonstrate that berberines has wide physiologic function and has great potential for structural modification as new drug lead.
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Discovery and optimization of selective inhibitors of protein arginine methyltransferase 5 by docking-based virtual screening

TL;DR: The discovery of DC_P33 is reported as a hit compound of PRMT5 inhibitor, identified by molecular docking based virtual screening and 3H-labeled radioactive methylation assays, and it exhibits anti-proliferation activities against Z-138, Maver-1, and Jeko-1 cancer cells.
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Using support vector classification for SAR of fentanyl derivatives.

TL;DR: The results indicated that the performance of the SVC model was better than those of PCA, ANN, and KNN models for this data and could be a promising tool for SAR research.