Junmei Wang
Impact in
- Computational Theory and Mathematics top 0.01%
- Computational Drug Discovery Methods
- Molecular Biology top 0.05%
- Protein Structure and Dynamics
- DNA and Nucleic Acid Chemistry
- RNA and protein synthesis mechanisms
- Receptor Mechanisms and Signaling
Papers in
-
- Protein Structure and Dynamics 60
- DNA and Nucleic Acid Chemistry 14
-
- Computational Drug Discovery Methods 79
- Co-authors
- Peter A. Kollman (14 shared papers)David A. Case (3 shared papers)James W. Caldwell (4 shared papers)Romain M. Wolf (2 shared papers)Tingjun Hou (52 shared papers)Piotr Cieplak (14 shared papers)Wei Wang (1 shared paper)Youyong Li (21 shared papers)
- Journals
- Journal of Chemical Information and Modeling (29 papers)The Journal of Physical Chemistry B (13 papers)Journal of Chemical Theory and Computation (12 papers)Journal of Computational Chemistry (11 papers)The Journal of Chemical Physics (6 papers)
- Partner nations
- ChinaUnited StatesFrance
In The Last Decade
Junmei Wang
404 papers receiving 46.4k citations
Junmei Wang's Hit Papers
Peers
Comparison fields: 5 of 205
- Computational Theory and Mathematics 7.6k
- Molecular Biology 24.7k
- Physical and Theoretical Chemistry 2.2k
- Spectroscopy 3.6k
- Organic Chemistry 6.0k
Countries citing papers authored by Junmei Wang
This map shows the geographic impact of Junmei 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 Junmei Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Junmei Wang more than expected).
Fields of papers citing papers by Junmei Wang
This network shows the impact of papers produced by Junmei 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 Junmei Wang. The network helps show where Junmei Wang may publish in the future.
Co-authors
The 25 scholars most cited alongside Junmei Wang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 419 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Development and testing of a general amber force field Hit paper breakdown → | 2004 | 15622 |
| 2 | Automatic atom type and bond type perception in molecular mechanical calculations Hit paper breakdown → | 2006 | 4518 |
| 3 | A point‐charge force field for molecular mechanics simulations of proteins based on condensed‐phase quantum mechanical calculations Hit paper breakdown → | 2003 | 3892 |
| 4 | How well does a restrained electrostatic potential (RESP) model perform in calculating conformational energies of organic and biological molecules? Hit paper breakdown → | 2000 | 3546 |
| 5 | Assessing the Performance of the MM/PBSA and MM/GBSA Methods. 1. The Accuracy of Binding Free Energy Calculations Based on Molecular Dynamics Simulations Hit paper breakdown → | 2010 | 2164 |
| 6 | End-Point Binding Free Energy Calculation with MM/PBSA and MM/GBSA: Strategies and Applications in Drug Design Hit paper breakdown → | 2019 | 1550 |
| 7 | Assessing the performance of the molecular mechanics/Poisson Boltzmann surface area and molecular mechanics/generalized Born surface area methods. II. The accuracy of ranking poses generated from docking Hit paper breakdown → | 2010 | 643 |
| 8 | Use of MM-PBSA in Reproducing the Binding Free Energies to HIV-1 RT of TIBO Derivatives and Predicting the Binding Mode to HIV-1 RT of Efavirenz by Docking and MM-PBSA Hit paper breakdown → | 2001 | 631 |
| 9 | A fast and high-quality charge model for the next generation general AMBER force field Hit paper breakdown → | 2020 | 459 |
| 10 | Assessing the Performance of MM/PBSA and MM/GBSA Methods. 3. The Impact of Force Fields and Ligand Charge Models Hit paper breakdown → | 2013 | 421 |
| 11 | The application of in silico drug-likeness predictions in pharmaceutical research Hit paper breakdown → | 2015 | 376 |
| 12 | Fast Identification of Possible Drug Treatment of Coronavirus Disease-19 (COVID-19) through Computational Drug Repurposing Study Hit paper breakdown → | 2020 | 358 |
| 13 | 2016 | 322 | |
| 14 | 2006 | 313 | |
| 15 | 2009 | 268 | |
| 16 | 2011 | 244 | |
| 17 | 2004 | 228 | |
| 18 | 2016 | 214 | |
| 19 | 2011 | 211 | |
| 20 | 2012 | 190 |
About Junmei Wang
Junmei Wang is a scholar working on Molecular Biology, Computational Theory and Mathematics, Materials Chemistry, Genetics and Spectroscopy, having authored 419 papers that have together received 46.8k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (79 papers), Protein Structure and Dynamics (60 papers), Glioma Diagnosis and Treatment (25 papers), Machine Learning in Materials Science (19 papers), Analytical Chemistry and Chromatography (17 papers), Pharmacogenetics and Drug Metabolism (14 papers), Enzyme Structure and Function (14 papers) and DNA and Nucleic Acid Chemistry (14 papers). The work is most often cited by research in Computational Theory and Mathematics (7.6k citations), Molecular Biology (24.7k citations), Physical and Theoretical Chemistry (2.2k citations), Spectroscopy (3.6k citations) and Organic Chemistry (6.0k citations). Junmei Wang has collaborated with scholars based in China, United States and France. Frequent co-authors include Peter A. Kollman, David A. Case, James W. Caldwell, Romain M. Wolf, Tingjun Hou, Piotr Cieplak, Wei Wang, Youyong Li, Wei Wang and Yong Duan. Their work appears in journals such as Journal of Chemical Information and Modeling, The Journal of Physical Chemistry B, Journal of Chemical Theory and Computation, Journal of Computational Chemistry and The Journal of Chemical Physics.
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.