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Wen-Jing Wang
Researcher at Sichuan University
Publications - 13
Citations - 764
Wen-Jing Wang is an academic researcher from Sichuan University. The author has contributed to research in topics: Structure–activity relationship & Virtual screening. The author has an hindex of 11, co-authored 13 publications receiving 334 citations.
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Journal ArticleDOI
Small molecules in targeted cancer therapy: advances, challenges, and future perspectives.
Lei Zhong,Y. Li,Liang Xiong,Wen-Jing Wang,Ming Wu,Ting Yuan,Wei Yang,Chenyu Tian,Zhuang Miao,Tianqi Wang,Shengyong Yang +10 more
TL;DR: A comprehensive review of small-molecule targeted anti-cancer drugs according to the target classification is conducted, which presents all the approved drugs as well as important drug candidates in clinical trials for each target, and discusses the current challenges.
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ID-Score: A New Empirical Scoring Function Based on a Comprehensive Set of Descriptors Related to Protein–Ligand Interactions
TL;DR: The better performance of ID-Score enables it as a useful tool in assessing protein-ligand binding affinity in structure-based drug discovery as well as in lead optimization.
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Structural optimization and structure-activity relationships of N2-(4-(4-Methylpiperazin-1-yl)phenyl)-N8-phenyl-9H-purine-2,8-diamine derivatives, a new class of reversible kinase inhibitors targeting both EGFR-activating and resistance mutations.
Jiao Yang,Li-Jiao Wang,Jing-Jing Liu,Lei Zhong,Ren-Lin Zheng,Yong Xu,Pan Ji,Chun-Hui Zhang,Wen-Jing Wang,Xing-Dong Lin,Lin-Li Li,Yuquan Wei,Shengyong Yang +12 more
TL;DR: Structural optimization of a hit compound led to the identification of compound 9e, a highly potent kinase inhibitor against both EGFR-activating and resistance mutations and has good kinase spectrum selectivity across the kinome.
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Synthesis and biological evaluation of novel (E)-N′-(2,3-dihydro-1H-inden-1-ylidene) benzohydrazides as potent LSD1 inhibitors
TL;DR: The results suggest that these optimized compounds might be served as promising LSD1 inhibitors against cancer, which merit further study.
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A combined molecular docking-based and pharmacophore-based target prediction strategy with a probabilistic fusion method for target ranking
TL;DR: Evaluations showed that the combined strategy consistently outperformed the sole use of docking-based and pharmacophore-based methods and was a possible way for improving the accuracy of in silico target prediction and a method for target ranking.