Miru Tang

465 total citations
13 papers, 298 citations indexed

About

Miru Tang is a scholar working on Materials Chemistry, Molecular Biology and Catalysis. According to data from OpenAlex, Miru Tang has authored 13 papers receiving a total of 298 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Materials Chemistry, 7 papers in Molecular Biology and 5 papers in Catalysis. Recurrent topics in Miru Tang's work include Catalytic Processes in Materials Science (5 papers), Catalysis and Oxidation Reactions (5 papers) and Computational Drug Discovery Methods (3 papers). Miru Tang is often cited by papers focused on Catalytic Processes in Materials Science (5 papers), Catalysis and Oxidation Reactions (5 papers) and Computational Drug Discovery Methods (3 papers). Miru Tang collaborates with scholars based in United States, China and Germany. Miru Tang's co-authors include Qingfeng Ge, Zhenrong Zhang, Zdenek Dohnálek, Jinsai Shang, Hongming Chen, Baiqing Li, Igor Lyubinetsky, Zhi‐Tao Wang, Hua Lin and Douglas J. Kojetin and has published in prestigious journals such as Journal of the American Chemical Society, SHILAP Revista de lepidopterología and Analytical Chemistry.

In The Last Decade

Miru Tang

13 papers receiving 295 citations

Peers

Miru Tang
Sergiy Tyukhtenko United States
B. Mamat Germany
Yugang Zhang United States
Zenan Li China
Ho Jin Lee South Korea
Sunil K. Pandey India
Jianzhao Peng China
Amanda S. Byer United States
Sergiy Tyukhtenko United States
Miru Tang
Citations per year, relative to Miru Tang Miru Tang (= 1×) peers Sergiy Tyukhtenko

Countries citing papers authored by Miru Tang

Since Specialization
Citations

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

Fields of papers citing papers by Miru Tang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Miru Tang

This figure shows the co-authorship network connecting the top 25 collaborators of Miru Tang. A scholar is included among the top collaborators of Miru Tang 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 Miru Tang. Miru Tang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

13 of 13 papers shown
1.
Qin, Yifei, Xue-Xin Wei, Mingyuan Xu, et al.. (2025). Comprehensive Benchmark Study of Diffusion-Based 3D Molecular Generation Models. ACS Omega. 10(37). 42760–42775. 1 indexed citations
2.
Li, Wenqi, Bin Wu, Yi Xia, et al.. (2025). Discovery of Fluorescent Probe ABDS-2 for Farnesoid X Receptor Modulator Characterization and Cell-Based Imaging. Analytical Chemistry. 97(4). 2019–2027. 1 indexed citations
3.
Zhang, Jiyun, Miru Tang, & Jinsai Shang. (2024). PPARγ Modulators in Lung Cancer: Molecular Mechanisms, Clinical Prospects, and Challenges. Biomolecules. 14(2). 190–190. 13 indexed citations
4.
Tang, Miru, et al.. (2023). Discovery of novel A2AR antagonists through deep learning-based virtual screening. SHILAP Revista de lepidopterología. 3. 100058–100058. 7 indexed citations
5.
Tang, Miru, Baiqing Li, & Hongming Chen. (2023). Application of message passing neural networks for molecular property prediction. Current Opinion in Structural Biology. 81. 102616–102616. 22 indexed citations
6.
Shang, Jinsai, Richard Brust, Sarah A. Mosure, et al.. (2018). Cooperative cobinding of synthetic and natural ligands to the nuclear receptor PPARγ. eLife. 7. 61 indexed citations
7.
Tang, Miru & Qingfeng Ge. (2017). Mechanistic understanding on oxygen evolution reaction on γ-FeOOH (010) under alkaline condition based on DFT computational study. CHINESE JOURNAL OF CATALYSIS (CHINESE VERSION). 38(9). 1621–1628. 25 indexed citations
8.
Yu, Xiaojuan, Zhenrong Zhang, Chengwu Yang, et al.. (2016). Interaction of Formaldehyde with the Rutile TiO2(110) Surface: A Combined Experimental and Theoretical Study. The Journal of Physical Chemistry C. 120(23). 12626–12636. 53 indexed citations
9.
Tang, Miru, Zhenrong Zhang, & Qingfeng Ge. (2016). A DFT-based study of surface chemistries of rutile TiO2 and SnO2(110) toward formaldehyde and formic acid. Catalysis Today. 274. 103–108. 21 indexed citations
10.
Zhu, Ke, Miru Tang, Zhi‐Tao Wang, et al.. (2015). Tracking Site-Specific C–C Coupling of Formaldehyde Molecules on Rutile TiO2(110). The Journal of Physical Chemistry C. 119(25). 14267–14272. 20 indexed citations
11.
Tang, Miru, Zhi‐Tao Wang, Igor Lyubinetsky, et al.. (2015). Low-Temperature Reductive Coupling of Formaldehyde on Rutile TiO2(110). The Journal of Physical Chemistry C. 119(32). 18452–18457. 19 indexed citations
12.
Zhang, Zhenrong, Miru Tang, Zhi‐Tao Wang, et al.. (2014). Imaging of Formaldehyde Adsorption and Diffusion on TiO2(110). Topics in Catalysis. 58(2-3). 103–113. 28 indexed citations
13.
Huang, Jing, Xiaolong Zheng, Zhibin Song, et al.. (2010). Highly Selective Suppression of Melanoma Cells by Inducible DNA Cross-Linking Agents: Bis(catechol) Derivatives. Journal of the American Chemical Society. 132(43). 15321–15327. 27 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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