Run‐Ling Wang

2.3k total citations
77 papers, 1.2k citations indexed

About

Run‐Ling Wang is a scholar working on Molecular Biology, Immunology and Organic Chemistry. According to data from OpenAlex, Run‐Ling Wang has authored 77 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 59 papers in Molecular Biology, 23 papers in Immunology and 20 papers in Organic Chemistry. Recurrent topics in Run‐Ling Wang's work include Protein Tyrosine Phosphatases (38 papers), Galectins and Cancer Biology (22 papers) and Bioactive Compounds and Antitumor Agents (15 papers). Run‐Ling Wang is often cited by papers focused on Protein Tyrosine Phosphatases (38 papers), Galectins and Cancer Biology (22 papers) and Bioactive Compounds and Antitumor Agents (15 papers). Run‐Ling Wang collaborates with scholars based in China, New Zealand and United States. Run‐Ling Wang's co-authors include Ying Ma, Weiren Xu, Shuqing Wang, Kuo‐Chen Chou, Xian‐Chao Cheng, Hua Sun, Peng Yu, Weiya Li, Wei‐Li Dong and Xiaotong Song and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Journal of Agricultural and Food Chemistry.

In The Last Decade

Run‐Ling Wang

76 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Run‐Ling Wang China 20 782 322 193 184 181 77 1.2k
Theodore Johnson United States 15 936 1.2× 454 1.4× 127 0.7× 213 1.2× 109 0.6× 17 1.5k
Weiren Xu China 22 902 1.2× 332 1.0× 205 1.1× 106 0.6× 131 0.7× 98 1.5k
Bruce G. Szczepankiewicz United States 19 904 1.2× 386 1.2× 102 0.5× 282 1.5× 80 0.4× 32 1.4k
Jérémie Mortier Germany 22 663 0.8× 353 1.1× 246 1.3× 77 0.4× 54 0.3× 37 1.2k
Sangita B. Patel United States 26 1.1k 1.4× 546 1.7× 234 1.2× 171 0.9× 185 1.0× 39 1.7k
Shin‐Hun Juang Taiwan 25 814 1.0× 360 1.1× 81 0.4× 315 1.7× 60 0.3× 69 2.0k
Philipp Saiko Austria 22 911 1.2× 294 0.9× 54 0.3× 104 0.6× 79 0.4× 56 1.9k
Temitope Isaac Adelusi Nigeria 16 427 0.5× 166 0.5× 218 1.1× 60 0.3× 77 0.4× 45 851
Dilep Kumar Sigalapalli India 20 508 0.6× 717 2.2× 119 0.6× 122 0.7× 72 0.4× 48 1.2k

Countries citing papers authored by Run‐Ling Wang

Since Specialization
Citations

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

Fields of papers citing papers by Run‐Ling Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Run‐Ling Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Run‐Ling Wang. A scholar is included among the top collaborators of Run‐Ling 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 Run‐Ling Wang. Run‐Ling 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.
Ma, Ying, Weiya Li, Ting Sun, et al.. (2022). Structure-based discovery of a specific SHP2 inhibitor with enhanced blood–brain barrier penetration from PubChem database. Bioorganic Chemistry. 121. 105648–105648. 2 indexed citations
2.
Ma, Ying, et al.. (2021). Design, synthesis, biological evaluation and molecular dynamics of LAR inhibitors. Computational Biology and Chemistry. 92. 107481–107481. 3 indexed citations
3.
Lu, Xinhua, et al.. (2021). Exploring the dynamic mechanism of allosteric drug SHP099 inhibiting SHP2E69K. Molecular Diversity. 25(3). 1873–1887. 5 indexed citations
4.
Li, Haoxin, Wenyu Yang, Lipeng Li, et al.. (2021). Molecular dynamics study of CDC25BR492L mutant causing the activity decrease of CDC25B. Journal of Molecular Graphics and Modelling. 109. 108030–108030. 2 indexed citations
5.
Zhao, Guilong, et al.. (2020). Allosteric Inhibitors of SHP2: An Updated Patent Review (2015-2020). Current Medicinal Chemistry. 28(19). 3825–3842. 17 indexed citations
6.
Zhang, Zhao, et al.. (2020). Exploring the mechanism of the potent allosteric inhibitor compound2 on SHP2 WT and SHP2F285S by molecular dynamics study. Journal of Molecular Graphics and Modelling. 103. 107807–107807. 4 indexed citations
7.
Zhou, Hui, et al.. (2020). Identification of protein tyrosine phosphatase 1B (PTP1B) inhibitors through De Novo Evoluton, synthesis, biological evaluation and molecular dynamics simulation. Biochemical and Biophysical Research Communications. 526(1). 273–280. 12 indexed citations
8.
Tan, Xiaoli, et al.. (2019). Efficient Synthesis and Characterization of PEGylated/Deuterated Derivatives of Monophosphonated Tetrathiatriarylmethyl Radicals. SHILAP Revista de lepidopterología. 1 indexed citations
9.
10.
Liu, Wen‐Shan, Ruirui Wang, Yue Hai, et al.. (2019). Design, synthesis, biological evaluation and molecular dynamics studies of 4-thiazolinone derivatives as protein tyrosine phosphatase 1B (PTP1B) inhibitors. Journal of Biomolecular Structure and Dynamics. 38(13). 3814–3824. 14 indexed citations
11.
12.
Liu, Wen‐Shan, et al.. (2019). Investigating the reason for loss-of-function of Src homology 2 domain-containing protein tyrosine phosphatase 2 (SHP2) caused by Y279C mutation through molecular dynamics simulation. Journal of Biomolecular Structure and Dynamics. 38(9). 2509–2520. 3 indexed citations
13.
Lu, Xinhua, et al.. (2019). Exploring the effect of aplidin on low molecular weight protein tyrosine phosphatase by molecular docking and molecular dynamic simulation study. Computational Biology and Chemistry. 83. 107123–107123. 1 indexed citations
14.
15.
Wang, Meiyan, Yuanyuan Jin, Lisong Zhang, et al.. (2015). Synthesis, biological evaluation and 3D-QSAR studies of imidazolidine-2,4-dione derivatives as novel protein tyrosine phosphatase 1B inhibitors. European Journal of Medicinal Chemistry. 103. 91–104. 26 indexed citations
16.
Wen, Liuqing, Lanlan Zang, Kenneth Huang, et al.. (2015). Efficient enzymatic synthesis of l-rhamnulose and l-fuculose. Bioorganic & Medicinal Chemistry Letters. 26(3). 969–972. 16 indexed citations
17.
Ma, Ying, Xuejiao Wang, Yuanyuan Jin, et al.. (2014). Design Potential Selective Inhibitors for Treating Cancer by Targeting the Src Homology 2 (SH2) Domain-Containing Phosphatase 2 (Shp2) with Core Hopping Approach. Protein and Peptide Letters. 21(6). 556–563. 11 indexed citations
18.
Liang, Changyong, et al.. (2014). IT USAGE BEHAVIOR OF MEDICAL PERSONNEL: AN EMPIRICAL STUDY BASED ON THE THEORY OF PLANNED BEHAVIOR. Journal of the Association for Information Systems. 212.
19.
Ma, Ying, Shuqing Wang, Weiren Xu, Run‐Ling Wang, & Kuo‐Chen Chou. (2012). Design Novel Dual Agonists for Treating Type-2 Diabetes by Targeting Peroxisome Proliferator-Activated Receptors with Core Hopping Approach. PLoS ONE. 7(6). e38546–e38546. 98 indexed citations
20.
Zhang, Lisong, Shuqing Wang, Weiren Xu, Run‐Ling Wang, & Jingfang Wang. (2012). Scaffold-Based Pan-Agonist Design for the PPARα, PPARβ and PPARγ Receptors. PLoS ONE. 7(10). e48453–e48453. 13 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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