Jun Wang

14.5k total citations · 5 hit papers
257 papers, 9.9k citations indexed

About

Jun Wang is a scholar working on Molecular Biology, Epidemiology and Infectious Diseases. According to data from OpenAlex, Jun Wang has authored 257 papers receiving a total of 9.9k indexed citations (citations by other indexed papers that have themselves been cited), including 128 papers in Molecular Biology, 74 papers in Epidemiology and 49 papers in Infectious Diseases. Recurrent topics in Jun Wang's work include Influenza Virus Research Studies (59 papers), RNA and protein synthesis mechanisms (36 papers) and SARS-CoV-2 and COVID-19 Research (34 papers). Jun Wang is often cited by papers focused on Influenza Virus Research Studies (59 papers), RNA and protein synthesis mechanisms (36 papers) and SARS-CoV-2 and COVID-19 Research (34 papers). Jun Wang collaborates with scholars based in United States, China and Greece. Jun Wang's co-authors include Chunlong Ma, Yanmei Hu, William F. DeGrado, Mei Hong, Michael T. Marty, Julia A. Townsend, Giulio Maria Pasinetti, Yu Chen, Sarah D. Cady and Cinque Soto and has published in prestigious journals such as Nature, Science and Proceedings of the National Academy of Sciences.

In The Last Decade

Jun Wang

243 papers receiving 9.8k citations

Hit Papers

Boceprevir, GC-376, and ... 2006 2026 2012 2019 2020 2006 2010 2023 2024 200 400 600

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jun Wang United States 49 4.4k 2.3k 2.3k 1.7k 1.3k 257 9.9k
Wei Zheng United States 57 6.0k 1.4× 1.7k 0.7× 1.2k 0.5× 1.3k 0.8× 1.1k 0.8× 382 12.6k
Anton Simeonov United States 65 8.4k 1.9× 1.3k 0.6× 894 0.4× 2.4k 1.4× 1.5k 1.1× 298 14.1k
Dong‐Qing Wei China 57 6.8k 1.6× 1.9k 0.8× 946 0.4× 2.5k 1.5× 793 0.6× 584 12.9k
Lorenza Bordoli Switzerland 20 15.3k 3.5× 2.0k 0.9× 1.6k 0.7× 1.3k 0.8× 1.0k 0.8× 23 24.1k
Gabriel Studer Switzerland 17 9.5k 2.2× 1.5k 0.6× 1.1k 0.5× 977 0.6× 676 0.5× 22 15.2k
Stefan Bienert Switzerland 9 8.7k 2.0× 1.4k 0.6× 1.0k 0.4× 814 0.5× 641 0.5× 12 14.1k
Martino Bertoni Switzerland 11 8.4k 1.9× 1.3k 0.6× 955 0.4× 904 0.5× 577 0.4× 17 13.5k
Jason K. Perry United States 28 5.5k 1.3× 2.1k 0.9× 708 0.3× 3.1k 1.8× 1.9k 1.4× 60 10.3k
Sanjeev Krishna United Kingdom 63 4.3k 1.0× 2.1k 0.9× 2.5k 1.1× 2.1k 1.2× 679 0.5× 274 16.1k
David B. Ascher Australia 46 6.9k 1.6× 1.4k 0.6× 706 0.3× 2.6k 1.5× 1.7k 1.3× 187 11.8k

Countries citing papers authored by Jun Wang

Since Specialization
Citations

This map shows the geographic impact of Jun 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 Jun Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Wang more than expected).

Fields of papers citing papers by Jun Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jun 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 Jun Wang. The network helps show where Jun Wang may publish in the future.

Co-authorship network of co-authors of Jun Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Jun Wang. A scholar is included among the top collaborators of Jun 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 Jun Wang. Jun 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.
Jadhav, Prakash D., Bin Tan, Haozhou Tan, et al.. (2025). Design of quinoline SARS-CoV-2 papain-like protease inhibitors as oral antiviral drug candidates. Nature Communications. 16(1). 1604–1604. 8 indexed citations
2.
Lewandowski, Eric M., Xiujun Zhang, Haozhou Tan, et al.. (2025). Distal protein-protein interactions contribute to nirmatrelvir resistance. Nature Communications. 16(1). 1266–1266. 3 indexed citations
3.
Wang, Jun, Huimin Zhang, Ying Wang, et al.. (2024). Preparation and arc erosion mechanism of Ni skeleton reinforced Ag-based contact materials with CuO-coated SnO2. Ceramics International. 50(22). 47202–47214. 7 indexed citations
4.
Tan, Bin, Prakash D. Jadhav, Haozhou Tan, et al.. (2024). Design of a SARS-CoV-2 papain-like protease inhibitor with antiviral efficacy in a mouse model. Science. 383(6690). 1434–1440. 43 indexed citations breakdown →
6.
Wang, Chengbo, et al.. (2023). Alkylamides from Zanthoxylum armatum DC. and their neuroprotective activity. Phytochemistry. 211. 113704–113704. 7 indexed citations
7.
Gao, Yushan, Junyao Zhang, Dapeng Liu, et al.. (2023). Artificial synapses based on organic electrochemical transistors with self-healing dielectric layers. Chinese Chemical Letters. 35(3). 108582–108582. 15 indexed citations
8.
Wang, Kai, Zihao Guo, Ting Zeng, et al.. (2023). Transmission Characteristics and Inactivated Vaccine Effectiveness Against Transmission of SARS-CoV-2 Omicron BA.5 Variants in Urumqi, China. JAMA Network Open. 6(3). e235755–e235755. 17 indexed citations
9.
Ji, Tian, et al.. (2023). Research into the Beetle Antennae Optimization-Based PID Servo System Control of an Industrial Robot. Mathematics. 11(19). 4066–4066. 4 indexed citations
10.
Sun, Siyuan, Jun Wang, Fengming Li, et al.. (2023). Improved in situ characterization of protein complex dynamics at scale with thermal proximity co-aggregation. Nature Communications. 14(1). 7697–7697. 11 indexed citations
12.
Wang, Jun, Ben Yang, Shilei Dai, et al.. (2023). Weak Light‐Stimulated Synaptic Transistors Based on MoS2/Organic Semiconductor Heterojunction for Neuromorphic Computing. Advanced Materials Technologies. 8(16). 22 indexed citations
13.
Hu, Yanmei, et al.. (2022). Point-of-care SARS-CoV-2 sensing using lens-free imaging and a deep learning-assisted quantitative agglutination assay. Lab on a Chip. 22(19). 3744–3754. 19 indexed citations
14.
Wang, Jun, Li Sui, Linglin Feng, et al.. (2021). Enhanced mucosal penetration and efficient inhibition efficacy against cervical cancer of PEGylated docetaxel nanocrystals by TAT modification. Journal of Controlled Release. 336. 572–582. 35 indexed citations
15.
Sun, Yaping, et al.. (2021). Correlation Between Polymorphisms of the SIRT1 Gene microRNA Target Sites and Diabetic Nephropathy. Genetic Testing and Molecular Biomarkers. 25(6). 387–398. 4 indexed citations
16.
Sacco, M., Chunlong Ma, Panagiotis Lagarias, et al.. (2020). Structure and inhibition of the SARS-CoV-2 main protease reveal strategy for developing dual inhibitors against M pro and cathepsin L. Science Advances. 6(50). 284 indexed citations
17.
Thomaston, Jessica L., Yibing Wu, Nicholas F. Polizzi, et al.. (2019). X-ray Crystal Structure of the Influenza A M2 Proton Channel S31N Mutant in Two Conformational States: An Open and Shut Case. Journal of the American Chemical Society. 141(29). 11481–11488. 21 indexed citations
18.
Wang, Jun, Wenlong Yang, Yinmao Dong, et al.. (2018). Reducing the actuation threshold by incorporating a nonliquid crystal chain into a liquid crystal elastomer. RSC Advances. 8(9). 4857–4866. 18 indexed citations
19.
Thomaston, Jessica L., et al.. (2018). Inhibitors of the M2 Proton Channel Engage and Disrupt Transmembrane Networks of Hydrogen-Bonded Waters. Journal of the American Chemical Society. 140(45). 15219–15226. 83 indexed citations
20.
Wang, Jun. (2011). Citizen's Satisfaction with Public Education Service in China:A Quantitative Analysis via Hierarchical Linear Model. 1 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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