Taijin Wang

540 total citations
25 papers, 447 citations indexed

About

Taijin Wang is a scholar working on Molecular Biology, Oncology and Organic Chemistry. According to data from OpenAlex, Taijin Wang has authored 25 papers receiving a total of 447 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Molecular Biology, 10 papers in Oncology and 7 papers in Organic Chemistry. Recurrent topics in Taijin Wang's work include Computational Drug Discovery Methods (7 papers), Synthesis and biological activity (5 papers) and Cancer therapeutics and mechanisms (4 papers). Taijin Wang is often cited by papers focused on Computational Drug Discovery Methods (7 papers), Synthesis and biological activity (5 papers) and Cancer therapeutics and mechanisms (4 papers). Taijin Wang collaborates with scholars based in China, United States and Australia. Taijin Wang's co-authors include Lijuan Chen, Zhuang Yang, Minghai Tang, Liang Ma, Dong Cao, Yuanyuan Zhou, Xiaoyan Wang, Wei Xiang, Ting Niu and Linhong He and has published in prestigious journals such as Scientific Reports, Journal of Medicinal Chemistry and European Journal of Medicinal Chemistry.

In The Last Decade

Taijin Wang

22 papers receiving 438 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Taijin Wang China 10 264 201 124 45 34 25 447
Gurubasavaraja Swamy Purawarga Matada India 16 182 0.7× 332 1.7× 114 0.9× 73 1.6× 31 0.9× 53 551
Heba A. Hassan Egypt 14 261 1.0× 387 1.9× 94 0.8× 50 1.1× 36 1.1× 31 595
Johannes Belmar United States 7 315 1.2× 230 1.1× 104 0.8× 56 1.2× 23 0.7× 7 493
Seyed–Omar Zaraei Egypt 14 201 0.8× 210 1.0× 70 0.6× 37 0.8× 31 0.9× 36 385
Yuya Oguro Japan 11 263 1.0× 205 1.0× 99 0.8× 40 0.9× 28 0.8× 15 450
Séverine Ravez France 13 355 1.3× 375 1.9× 96 0.8× 22 0.5× 17 0.5× 25 643
Shaun Fillery United Kingdom 11 334 1.3× 236 1.2× 121 1.0× 40 0.9× 16 0.5× 17 569
Elizabeth Ornelas United States 7 385 1.5× 94 0.5× 135 1.1× 35 0.8× 26 0.8× 9 456
Han-Li Huang Taiwan 12 277 1.0× 102 0.5× 98 0.8× 20 0.4× 24 0.7× 18 387
Aras Emdadi United States 10 359 1.4× 139 0.7× 71 0.6× 103 2.3× 24 0.7× 10 479

Countries citing papers authored by Taijin Wang

Since Specialization
Citations

This map shows the geographic impact of Taijin Wang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Taijin Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Taijin Wang more than expected).

Fields of papers citing papers by Taijin Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Taijin Wang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Taijin Wang. The network helps show where Taijin Wang may publish in the future.

Co-authorship network of co-authors of Taijin Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Taijin Wang. A scholar is included among the top collaborators of Taijin Wang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Taijin Wang. Taijin Wang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Guo, Tao, Yurong Zou, Minghai Tang, et al.. (2025). Discovery and Pharmacological Evaluation of Potent and Highly Selective PARP1 Inhibitors. Journal of Medicinal Chemistry. 68(13). 13491–13515. 3 indexed citations
2.
Guo, Tao, Zhiyuan Fu, Qiantao Wang, et al.. (2025). A structure-based framework for selective inhibitor design and optimization. Communications Biology. 8(1). 422–422.
3.
Guo, Tao, Tao Yang, Minghai Tang, et al.. (2025). Design, Synthesis, and Pharmacodynamic Evaluation of Highly Selective PARP1 Inhibitors with Brain Penetrance. Journal of Medicinal Chemistry. 68(2). 1731–1754. 3 indexed citations
4.
Zou, Yurong, Tao Guo, Zhiyuan Fu, et al.. (2025). Predicting the Brain-To-Plasma Unbound Partition Coefficient of Compounds via Formula-Guided Network. Journal of Chemical Information and Modeling. 65(10). 5099–5112.
5.
Zou, Yurong, Tao Yang, Tao Guo, et al.. (2024). Local Scaffold Diversity-Contributed Generator for Discovering Potential NLRP3 Inhibitors. Journal of Chemical Information and Modeling. 64(3). 737–748. 4 indexed citations
6.
Pei, Heying, Linhong He, Mingfeng Shao, et al.. (2018). Discovery of a highly selective JAK3 inhibitor for the treatment of rheumatoid arthritis. Scientific Reports. 8(1). 5273–5273. 33 indexed citations
7.
Yan, Wei, Tao Yang, Jianhong Yang, et al.. (2018). SKLB060 Reversibly Binds to Colchicine Site of Tubulin and Possesses Efficacy in Multidrug-Resistant Cell Lines. Cellular Physiology and Biochemistry. 47(2). 489–504. 31 indexed citations
8.
He, Linhong, et al.. (2018). Design, synthesis, and SAR study of highly potent, selective, irreversible covalent JAK3 inhibitors. Molecular Diversity. 22(2). 343–358. 14 indexed citations
9.
Wang, Taijin, Zhuang Yang, Yongguang Zhang, et al.. (2017). Discovery of novel CDK8 inhibitors using multiple crystal structures in docking-based virtual screening. European Journal of Medicinal Chemistry. 129. 275–286. 26 indexed citations
10.
Zhou, Yuanyuan, Wei Yan, Dong Cao, et al.. (2017). Design, synthesis and biological evaluation of 4-anilinoquinoline derivatives as novel potent tubulin depolymerization agents. European Journal of Medicinal Chemistry. 138. 1114–1125. 29 indexed citations
11.
12.
Tang, Huan, Heying Pei, Taijin Wang, et al.. (2016). Flavonoids and biphenylneolignans with anti-inflammatory activity from the stems of Millettia griffithii. Bioorganic & Medicinal Chemistry Letters. 26(18). 4417–4422. 11 indexed citations
13.
Cao, Dong, Xiaoyan Wang, Lei Lei, et al.. (2016). Synthesis, in vitro and in vivo evaluation of novel substituted N-(4-(2-(4-benzylpiperazin-1-yl)ethoxy)phenyl)-N-methyl-quinazolin-4-amines as potent antitumor agents. Bioorganic & Medicinal Chemistry Letters. 26(8). 1931–1935. 3 indexed citations
14.
Ma, Liang, Taijin Wang, Min Shi, & Haoyu Ye. (2016). Synthesis, activity, and docking study of phenylthiazole acids as potential agonists of PPARγ. Drug Design Development and Therapy. 10. 1807–1807. 3 indexed citations
15.
Chen, Yong, Xiaoyan Wang, Wei Xiang, et al.. (2016). Development of Purine-Based Hydroxamic Acid Derivatives: Potent Histone Deacetylase Inhibitors with Marked in Vitro and in Vivo Antitumor Activities. Journal of Medicinal Chemistry. 59(11). 5488–5504. 56 indexed citations
16.
Ma, Liang, et al.. (2016). Synthesis, Activity, and Docking Study of Novel Phenylthiazole‐Carboxamido Acid Derivatives as FFA2 Agonists. Chemical Biology & Drug Design. 88(1). 26–37. 6 indexed citations
17.
Wu, Wenshuang, Buyun Ma, Haoyu Ye, et al.. (2016). Millepachine, a potential topoisomerase II inhibitor induces apoptosis via activation of NF-κB pathway in ovarian cancer. Oncotarget. 7(32). 52281–52293. 22 indexed citations
18.
Zhou, Lu, et al.. (2015). Discovery of Novel NAMPT Inhibitors Based on Pharmacophore Modeling and Virtual Screening Techniques. Combinatorial Chemistry & High Throughput Screening. 17(10). 868–878. 1 indexed citations
19.
20.
Wang, Taijin, et al.. (2013). Applications of 3D-QSAR and structure-based pharmacophore modeling, virtual screening, ADMET, and molecular docking of putative MAPKAP-K2 (MK2) inhibitors. Medicinal Chemistry Research. 22(10). 4818–4829. 2 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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