Kevin Hauser

12.5k total citations · 3 hit papers
13 papers, 8.6k citations indexed

About

Kevin Hauser is a scholar working on Molecular Biology, Infectious Diseases and Atomic and Molecular Physics, and Optics. According to data from OpenAlex, Kevin Hauser has authored 13 papers receiving a total of 8.6k indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 2 papers in Infectious Diseases and 2 papers in Atomic and Molecular Physics, and Optics. Recurrent topics in Kevin Hauser's work include Protein Structure and Dynamics (4 papers), DNA and Nucleic Acid Chemistry (3 papers) and RNA and protein synthesis mechanisms (3 papers). Kevin Hauser is often cited by papers focused on Protein Structure and Dynamics (4 papers), DNA and Nucleic Acid Chemistry (3 papers) and RNA and protein synthesis mechanisms (3 papers). Kevin Hauser collaborates with scholars based in United States, Switzerland and France. Kevin Hauser's co-authors include Carlos Simmerling, Lauren Wickstrom, Koushik Kasavajhala, David Veesler, Matthew McCallum, Davide Corti, Josh R. Dillen, Gyorgy Snell, Laura E. Rosen and Lingle Wang and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and Nucleic Acids Research.

In The Last Decade

Kevin Hauser

13 papers receiving 8.5k 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.

ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB

2015 7.9k citations Koushik Kasavajhala, Lauren Wickstrom et al. Journal of Chemical Theory and Computation profile →
Structural basis of SARS-CoV-2 Omicron immune evasion and receptor engagement

2022 311 citations Matthew McCallum, Nadine Czudnochowski et al. Science profile →
Shifting mutational constraints in the SARS-CoV-2 receptor-binding domain during viral evolution

2022 157 citations Tyler N. Starr, Allison J. Greaney et al. Science profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Kevin Hauser United States	9	5.9k	1.2k	1.1k	1.0k	771	13	8.6k
Koushik Kasavajhala United States	7	6.7k 1.1×	1.4k 1.1×	1.3k 1.2×	682 0.7×	900 1.2×	9	9.4k
Lauren Wickstrom United States	17	6.3k 1.1×	1.3k 1.0×	1.3k 1.2×	630 0.6×	817 1.1×	23	8.8k
Jason Swails United States	15	7.2k 1.2×	1.6k 1.3×	1.5k 1.3×	719 0.7×	989 1.3×	19	10.3k
Daniel R. Roe United States	20	5.4k 0.9×	1.1k 0.9×	1.1k 1.0×	522 0.5×	766 1.0×	38	7.7k
Lillian T. Chong United States	24	4.6k 0.8×	1.2k 0.9×	880 0.8×	616 0.6×	615 0.8×	58	6.3k
Jing Huang China	32	7.2k 1.2×	1.1k 0.9×	1.8k 1.6×	890 0.9×	761 1.0×	135	11.0k
Dina Schneidman‐Duhovny United States	41	7.2k 1.2×	1.3k 1.0×	1.8k 1.6×	1.1k 1.1×	543 0.7×	84	10.0k
Elmar Krieger Netherlands	34	7.1k 1.2×	934 0.7×	1.2k 1.1×	557 0.6×	805 1.0×	45	11.6k
Yifei Qi United States	28	6.0k 1.0×	807 0.6×	680 0.6×	497 0.5×	652 0.8×	70	8.1k
Mark B. Swindells United Kingdom	27	5.2k 0.9×	1.0k 0.8×	1.1k 1.0×	597 0.6×	802 1.0×	50	7.6k

Countries citing papers authored by Kevin Hauser

Since Specialization

Citations

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

Fields of papers citing papers by Kevin Hauser

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kevin Hauser

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

All Works

Sort: Min cites: Since: Top N: Style:

13 of 13 papers shown

Zhang, Ivy, Dominic A. Rufa, Michael M. Henry, et al.. (2023). Identifying and Overcoming the Sampling Challenges in Relative Binding Free Energy Calculations of a Model Protein:Protein Complex. Journal of Chemical Theory and Computation. 19(15). 4863–4882. 10 indexed citations

Starr, Tyler N., Allison J. Greaney, William W. Hannon, et al.. (2022). Shifting mutational constraints in the SARS-CoV-2 receptor-binding domain during viral evolution. Science. 377(6604). 420–424. 157 indexed citations breakdown →

McCallum, Matthew, Nadine Czudnochowski, Laura E. Rosen, et al.. (2022). Structural basis of SARS-CoV-2 Omicron immune evasion and receptor engagement. Science. 375(6583). 864–868. 311 indexed citations breakdown →

Hauser, Kevin, et al.. (2022). Adaptive sparse sampling for quasiparticle interference imaging. MethodsX. 9. 101784–101784. 1 indexed citations

Clark, Anthony, Christopher Negron, Kevin Hauser, et al.. (2019). Relative Binding Affinity Prediction of Charge-Changing Sequence Mutations with FEP in Protein–Protein Interfaces. Journal of Molecular Biology. 431(7). 1481–1493. 68 indexed citations

Charlebois, Daniel A., et al.. (2018). Multiscale effects of heating and cooling on genes and gene networks. Proceedings of the National Academy of Sciences. 115(45). E10797–E10806. 41 indexed citations

Hauser, Kevin, Christopher Negron, Steven K. Albanese, et al.. (2018). Predicting resistance of clinical Abl mutations to targeted kinase inhibitors using alchemical free-energy calculations. Communications Biology. 1(1). 70–70. 64 indexed citations

Hauser, Kevin, Yiqing He, Miguel Garcı́a-Dı́az, Carlos Simmerling, & Evangelos A. Coutsias. (2017). Characterization of Biomolecular Helices and Their Complementarity Using Geometric Analysis. Journal of Chemical Information and Modeling. 57(4). 864–874. 8 indexed citations

Kasavajhala, Koushik, et al.. (2015). ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB. Journal of Chemical Theory and Computation. 11(8). 3696–3713. 7892 indexed citations breakdown →

10.

Hauser, Kevin, et al.. (2015). A Human Transcription Factor in Search Mode. Biophysical Journal. 108(2). 397a–397a. 1 indexed citations

11.

Hauser, Kevin, et al.. (2015). A human transcription factor in search mode. Nucleic Acids Research. 44(1). 63–74. 8 indexed citations

12.

Byrnes, James R., et al.. (2015). Base Flipping by MTERF1 Can Accommodate Multiple Conformations and Occurs in a Stepwise Fashion. Journal of Molecular Biology. 428(12). 2542–2556. 2 indexed citations

13.

Pogliani, Lionello, et al.. (1974). NMR investigation of proline and its derivatives. The proton magnetic resonance spectrum of L-proline at basic pH. Chemical Physics Letters. 27(3). 419–424. 18 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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