Brian Coventry

4.1k total citations · 3 hit papers
16 papers, 1.0k citations indexed

About

Brian Coventry is a scholar working on Molecular Biology, Infectious Diseases and Immunology. According to data from OpenAlex, Brian Coventry has authored 16 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Molecular Biology, 3 papers in Infectious Diseases and 3 papers in Immunology. Recurrent topics in Brian Coventry's work include RNA and protein synthesis mechanisms (5 papers), Protein Structure and Dynamics (4 papers) and Enzyme Structure and Function (3 papers). Brian Coventry is often cited by papers focused on RNA and protein synthesis mechanisms (5 papers), Protein Structure and Dynamics (4 papers) and Enzyme Structure and Function (3 papers). Brian Coventry collaborates with scholars based in United States, South Korea and China. Brian Coventry's 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 and has published in prestigious journals such as Nature, Science and Proceedings of the National Academy of Sciences.

In The Last Decade

Brian Coventry

15 papers receiving 1.0k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

De novo design of picomolar SARS-CoV-2 miniprotein inhibitors

2020 451 citations Longxing Cao, Inna Goreshnik et al. Science profile →
De novo design of luciferases using deep learning

2023 241 citations Hsien‐Wei Yeh, Christoffer Norn et al. Nature profile →
Improving de novo protein binder design with deep learning

2023 158 citations Nathaniel R. Bennett, Brian Coventry et al. Nature Communications profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Brian Coventry United States	10	713	278	145	133	105	16	1.0k
Inna Goreshnik United States	10	827 1.2×	301 1.1×	177 1.2×	130 1.0×	134 1.3×	14	1.1k
Gyu Rie Lee South Korea	14	748 1.0×	66 0.2×	99 0.7×	122 0.9×	148 1.4×	23	909
Jean Marc Kwasigroch Belgium	13	939 1.3×	99 0.4×	103 0.7×	97 0.7×	222 2.1×	19	1.2k
Rebecca F. Alford United States	6	990 1.4×	57 0.2×	152 1.0×	124 0.9×	230 2.2×	12	1.2k
Lorenzo Di Rienzo Italy	15	383 0.5×	112 0.4×	70 0.5×	102 0.8×	105 1.0×	39	578
Sebastian Kelm United Kingdom	17	888 1.2×	73 0.3×	486 3.4×	93 0.7×	139 1.3×	29	1.1k
Qingshan Fu China	16	569 0.8×	124 0.4×	70 0.5×	34 0.3×	32 0.3×	32	868
Sankar Basu India	15	614 0.9×	41 0.1×	59 0.4×	147 1.1×	194 1.8×	39	791
Pandjassarame Kangueane Singapore	16	726 1.0×	52 0.2×	81 0.6×	53 0.4×	61 0.6×	51	902
Leandro Radusky Spain	13	646 0.9×	88 0.3×	32 0.2×	85 0.6×	121 1.2×	20	819

Countries citing papers authored by Brian Coventry

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

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16 of 16 papers shown

Ragotte, Robert J., John Kit Chung Tam, Jacob M. Berman, et al.. (2025). De novo design of potent inhibitors of clostridial family toxins. Proceedings of the National Academy of Sciences. 122(39). e2509329122–e2509329122. 1 indexed citations

Glasscock, Cameron J., Ryan McHugh, Lindsey Doyle, et al.. (2025). Computational design of sequence-specific DNA-binding proteins. Nature Structural & Molecular Biology. 32(11). 2252–2261. 1 indexed citations

Kim, Hyojin, et al.. (2025). De novo design of protein minibinder agonists of TLR3. Nature Communications. 16(1). 1234–1234. 10 indexed citations

Ahern, Woody, Jason Yim, Doug Tischer, et al.. (2025). Atom-level enzyme active site scaffolding using RFdiffusion2. Nature Methods. 23(1). 96–105. 2 indexed citations

Wang, Xinru, Kaiyong Cai, Preetham Venkatesh, et al.. (2025). Tuning insulin receptor signaling using de novo-designed agonists. Molecular Cell. 85(21). 4064–4081.e9.

Krishnakumar, Aditya, Robert J. Ragotte, Inna Goreshnik, et al.. (2024). Target-conditioned diffusion generates potent TNFR superfamily antagonists and agonists. Science. 386(6726). 1154–1161. 19 indexed citations

Sun, Ke, Tongyue Wang, Jizhong Zhang, et al.. (2024). Accurate de novo design of heterochiral protein–protein interactions. Cell Research. 34(12). 846–858. 15 indexed citations

Bennett, Nathaniel R., Brian Coventry, Inna Goreshnik, et al.. (2023). Improving de novo protein binder design with deep learning. Nature Communications. 14(1). 2625–2625. 158 indexed citations breakdown →

Gerben, Stacey, Andrew J. Borst, Derrick R. Hicks, et al.. (2023). Design of Diverse Asymmetric Pockets in De Novo Homo-oligomeric Proteins. Biochemistry. 62(2). 358–368. 2 indexed citations

10.

Yeh, Hsien‐Wei, Christoffer Norn, Yakov Kipnis, et al.. (2023). De novo design of luciferases using deep learning. Nature. 614(7949). 774–780. 241 indexed citations breakdown →

11.

Trippe, Brian L., Buwei Huang, Erika A. DeBenedictis, et al.. (2022). Randomized gates eliminate bias in sort‐seq assays. Protein Science. 31(9). 4 indexed citations

12.

Seok, Jeong Ho, Insu Hwang, Jared Adolf‐Bryfogle, et al.. (2022). Computational design of a neutralizing antibody with picomolar binding affinity for all concerning SARS-CoV-2 variants. mAbs. 14(1). 13 indexed citations

13.

Hicks, Derrick R., Michelle DeWitt, Brian Coventry, et al.. (2022). De novo design of protein homodimers containing tunable symmetric protein pockets. Proceedings of the National Academy of Sciences. 119(30). e2113400119–e2113400119. 9 indexed citations

14.

Coventry, Brian & David Baker. (2021). Protein sequence optimization with a pairwise decomposable penalty for buried unsatisfied hydrogen bonds. PLoS Computational Biology. 17(3). e1008061–e1008061. 10 indexed citations

15.

Maguire, Jack B., Hugh K. Haddox, Devin Strickland, et al.. (2020). Perturbing the energy landscape for improved packing during computational protein design. Proteins Structure Function and Bioinformatics. 89(4). 436–449. 87 indexed citations

16.

Cao, Longxing, Inna Goreshnik, Brian Coventry, et al.. (2020). De novo design of picomolar SARS-CoV-2 miniprotein inhibitors. Science. 370(6515). 426–431. 451 indexed citations breakdown →

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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