Brian Coventry
- Molecular Biology top 10%
- Infectious Diseases top 5%
- Radiology, Nuclear Medicine and Imaging top 10%
- Computational Theory and Mathematics top 5%
- Materials Chemistry
- Co-authors
- David BakerLongxing CaoInna GoreshnikLauren CarterLance StewartRita E. ChenEva‐Maria StrauchL. M. Miller
- Topics
- RNA and protein synthesis mechanisms (5 papers)Protein Structure and Dynamics (4 papers)Enzyme Structure and Function (3 papers)
- Partner nations
- United StatesSouth KoreaChina
In The Last Decade
Brian Coventry
15 papers receiving 1.0k citations
Hit Papers
Peers
Comparison fields: 5 of 83
- Molecular Biology 713
- Infectious Diseases 278
- Radiology, Nuclear Medicine and Imaging 145
- Computational Theory and Mathematics 133
- Materials Chemistry 105
Countries citing papers authored by Brian Coventry
This map shows the geographic impact of Brian Coventry'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 Brian Coventry with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Brian Coventry more than expected).
Fields of papers citing papers by Brian Coventry
This network shows the impact of papers produced by Brian Coventry. 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 Brian Coventry. The network helps show where Brian Coventry may publish in the future.
Co-authorship network of co-authors of Brian Coventry
This figure shows the co-authorship network connecting the top 25 collaborators of Brian Coventry. A scholar is included among the top collaborators of Brian Coventry 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 Brian Coventry. Brian Coventry is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 10 | |
| 4 | 2 | |
| 5 | 0 | |
| 6 | 19 | |
| 7 | 15 | |
| 8 | Improving de novo protein binder design with deep learningbreakdown → | 158 |
| 9 | 2 | |
| 10 | De novo design of luciferases using deep learningbreakdown → | 241 |
| 11 | 4 | |
| 12 | 13 | |
| 13 | 9 | |
| 14 | 10 | |
| 15 | 87 | |
| 16 | De novo design of picomolar SARS-CoV-2 miniprotein inhibitorsbreakdown → | 451 |
About Brian Coventry
Brian Coventry is a scholar working on Molecular Biology, Biophysics and Infectious Diseases, having authored 16 papers that have together received 1.0k indexed citations. Recurring topics across this work include RNA and protein synthesis mechanisms (5 papers), Protein Structure and Dynamics (4 papers) and Enzyme Structure and Function (3 papers). The work is most often cited by research in Infectious Diseases (278 citations), Molecular Biology (713 citations) and Computational Theory and Mathematics (133 citations). Brian Coventry has collaborated with scholars based in United States, South Korea and China. Frequent co-authors include David Baker, Longxing Cao, Inna Goreshnik, Lauren Carter, Lance Stewart, Rita E. Chen, Eva‐Maria Strauch, L. M. Miller, Alexandra C. Walls and James Brett Case. Their work appears in journals such as Nature, Science and Proceedings of the National Academy of Sciences.
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.