Yongna Yuan
-
- Complex Network Analysis Techniques 9
- Opinion Dynamics and Social Influence 8
-
- Computational Drug Discovery Methods 12
- Environmental Chemistry top 10%
-
- Protein Structure and Dynamics 12
- RNA and protein synthesis mechanisms 5
- DNA and Nucleic Acid Chemistry 4
-
- Machine Learning in Materials Science 6
-
- Advanced Graph Neural Networks 4
- Co-authors
- Ruisheng ZhangRongjing HuPaul L. A. PopelierFan YangYabing YaoMatthew J. L. MillsJianxin TangChunyan Zhao
- Cited by
- Statistical and Nonlinear PhysicsComputational Theory and MathematicsEnvironmental Chemistry
- Journals
- Journal of Computational Chemistry (3 papers)Journal of Molecular Modeling (3 papers)International Journal of Modern Physics C (3 papers)
- Partner nations
- ChinaUnited KingdomFrance
In The Last Decade
Yongna Yuan
45 papers receiving 728 citations
Peers
Comparison fields: 5 of 119
- Statistical and Nonlinear Physics 226
- Computational Theory and Mathematics 105
- Environmental Chemistry 50
- Health, Toxicology and Mutagenesis 63
- Physical and Theoretical Chemistry 40
Countries citing papers authored by Yongna Yuan
This map shows the geographic impact of Yongna Yuan'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 Yongna Yuan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yongna Yuan more than expected).
Fields of papers citing papers by Yongna Yuan
This network shows the impact of papers produced by Yongna Yuan. 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 Yongna Yuan. The network helps show where Yongna Yuan may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Yongna Yuan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 3 | |
| 2 | 2024 | 2 | |
| 3 | 2024 | 1 | |
| 4 | 2024 | 1 | |
| 5 | 2024 | 1 | |
| 6 | 2023 | 10 | |
| 7 | 2023 | 2 | |
| 8 | 2021 | 14 | |
| 9 | 2021 | 3 | |
| 10 | 2020 | 5 | |
| 11 | 2020 | 18 | |
| 12 | 2019 | 10 | |
| 13 | 2019 | 12 | |
| 14 | 2018 | 29 | |
| 15 | 2018 | 40 | |
| 16 | 2016 | 6 | |
| 17 | 2014 | 11 | |
| 18 | Successful molecular dynamics and binding energy calculation of HIV-1 Tat complexed with P-TEFb | 2011 | 1 |
| 19 | 2010 | 24 | |
| 20 | 2008 | 21 |
About Yongna Yuan
Yongna Yuan is a scholar working on Computational Theory and Mathematics, Statistical and Nonlinear Physics and Biological Psychiatry, having authored 48 papers that have together received 737 indexed citations. Recurring topics across this work include Protein Structure and Dynamics (12 papers), Computational Drug Discovery Methods (12 papers), Complex Network Analysis Techniques (9 papers), Opinion Dynamics and Social Influence (8 papers), Machine Learning in Materials Science (6 papers), RNA and protein synthesis mechanisms (5 papers), DNA and Nucleic Acid Chemistry (4 papers) and Advanced Graph Neural Networks (4 papers). The work is most often cited by research in Statistical and Nonlinear Physics (226 citations), Computational Theory and Mathematics (105 citations) and Environmental Chemistry (50 citations). Yongna Yuan has collaborated with scholars based in China, United Kingdom and France. Frequent co-authors include Ruisheng Zhang, Rongjing Hu, Paul L. A. Popelier, Fan Yang, Yabing Yao, Matthew J. L. Mills, Jianxin Tang, Chunyan Zhao, Zhili Zhao and Frank Jensen. Their work appears in journals such as Journal of Computational Chemistry, Journal of Molecular Modeling, International Journal of Modern Physics C, Journal of Chemical Information and Modeling and Knowledge-Based Systems.
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.