John L. Klepeis

16.3k total citations · 4 hit papers
33 papers, 10.6k citations indexed

About

John L. Klepeis is a scholar working on Molecular Biology, Materials Chemistry and Atomic and Molecular Physics, and Optics. According to data from OpenAlex, John L. Klepeis has authored 33 papers receiving a total of 10.6k indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Molecular Biology, 11 papers in Materials Chemistry and 9 papers in Atomic and Molecular Physics, and Optics. Recurrent topics in John L. Klepeis's work include Protein Structure and Dynamics (17 papers), Enzyme Structure and Function (8 papers) and Advanced Chemical Physics Studies (6 papers). John L. Klepeis is often cited by papers focused on Protein Structure and Dynamics (17 papers), Enzyme Structure and Function (8 papers) and Advanced Chemical Physics Studies (6 papers). John L. Klepeis collaborates with scholars based in United States and Germany. John L. Klepeis's co-authors include David E. Shaw, Ron O. Dror, Kresten Lindorff‐Larsen, Stefano Piana, Paul Maragakis, Kim Palmö, Michael P. Eastwood, Yibing Shan, Huafeng Xu and Brent A. Gregersen and has published in prestigious journals such as Science, Journal of the American Chemical Society and The Journal of Chemical Physics.

In The Last Decade

John L. Klepeis

33 papers receiving 10.5k citations

Hit Papers

Improved side‐chain torsion potentials for the Amber ff99... 2006 2026 2012 2019 2010 2006 2006 2009 1000 2.0k 3.0k 4.0k
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.

  1. Improved side‐chain torsion potentials for the Amber ff99SB protein force field
    2010 4.7k citations Kresten Lindorff‐Larsen, Stefano Piana et al. Proteins Structure Function and Bioinformatics profile →
  2. Molecular dynamics---Scalable algorithms for molecular dynamics simulations on commodity clusters
    2006 1.9k citations K. J. Bowers, Federico D. Sacerdoti et al. profile →
  3. Scalable Algorithms for Molecular Dynamics Simulations on Commodity Clusters
    2006 1.2k citations K. J. Bowers, Huafeng Xu et al. profile →
  4. Long-timescale molecular dynamics simulations of protein structure and function
    2009 578 citations John L. Klepeis, Kresten Lindorff‐Larsen et al. Current Opinion in Structural Biology profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
John L. Klepeis United States 24 7.0k 1.9k 1.8k 970 819 33 10.6k
Jason Swails United States 15 7.2k 1.0× 1.5k 0.8× 1.6k 0.8× 989 1.0× 717 0.9× 19 10.3k
Michael P. Eastwood United States 27 6.2k 0.9× 1.8k 1.0× 1.4k 0.7× 802 0.8× 888 1.1× 42 8.8k
Tai‐Sung Lee United States 31 8.0k 1.1× 1.9k 1.0× 1.9k 1.0× 1.2k 1.3× 926 1.1× 76 11.8k
Lauren Wickstrom United States 17 6.3k 0.9× 1.3k 0.7× 1.3k 0.7× 817 0.8× 617 0.8× 23 8.8k
Amedeo Caflisch Switzerland 71 11.0k 1.6× 2.6k 1.4× 2.6k 1.4× 1.1k 1.2× 1.0k 1.3× 288 14.8k
Viktor Horn̆ák United States 29 6.7k 1.0× 1.8k 0.9× 1.1k 0.6× 651 0.7× 964 1.2× 48 8.6k
Yibing Shan United States 31 6.8k 1.0× 1.4k 0.7× 1.7k 0.9× 887 0.9× 686 0.8× 47 9.6k
Nathan Baker United States 42 10.6k 1.5× 2.2k 1.2× 1.3k 0.7× 855 0.9× 628 0.8× 114 15.4k
Kenno Vanommeslaeghe United States 25 6.6k 0.9× 1.9k 1.0× 1.6k 0.9× 1.6k 1.7× 1.0k 1.2× 38 11.2k
Olgun Guvench United States 28 5.7k 0.8× 1.6k 0.9× 1.2k 0.7× 1.4k 1.5× 764 0.9× 53 9.4k

Countries citing papers authored by John L. Klepeis

Since Specialization
Citations

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

Fields of papers citing papers by John L. Klepeis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John L. Klepeis. 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 John L. Klepeis. The network helps show where John L. Klepeis may publish in the future.

Co-authorship network of co-authors of John L. Klepeis

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

All Works

20 of 20 papers shown
1.
Sadigh, Babak, Siya Zhu, Phanish Suryanarayana, et al.. (2021). Accurate parameterization of the kinetic energy functional for calculations using exact-exchange. The Journal of Chemical Physics. 156(2). 24107–24107. 4 indexed citations
2.
Donchev, Alexander, Andrew G. Taube, Cory Hargus, et al.. (2021). Quantum chemical benchmark databases of gold-standard dimer interaction energies. Scientific Data. 8(1). 55–55. 69 indexed citations
3.
McGibbon, Robert T., Andrew G. Taube, Alexander Donchev, et al.. (2017). Improving the accuracy of Møller-Plesset perturbation theory with neural networks. The Journal of Chemical Physics. 147(16). 161725–161725. 82 indexed citations
4.
Piana, Stefano, John L. Klepeis, & David E. Shaw. (2014). Assessing the accuracy of physical models used in protein-folding simulations: quantitative evidence from long molecular dynamics simulations. Current Opinion in Structural Biology. 24. 98–105. 374 indexed citations
5.
Lindorff‐Larsen, Kresten, Stefano Piana, Kim Palmö, et al.. (2010). Improved side‐chain torsion potentials for the Amber ff99SB protein force field. Proteins Structure Function and Bioinformatics. 78(8). 1950–1958. 4652 indexed citations breakdown →
6.
Klepeis, John L., Kresten Lindorff‐Larsen, Ron O. Dror, & David E. Shaw. (2009). Long-timescale molecular dynamics simulations of protein structure and function. Current Opinion in Structural Biology. 19(2). 120–127. 578 indexed citations breakdown →
7.
Tu, Tiankai, Charles A. Rendleman, David W. Borhani, et al.. (2008). A scalable parallel framework for analyzing terascale molecular dynamics simulation trajectories. IEEE International Conference on High Performance Computing, Data, and Analytics. 56. 36 indexed citations
8.
Larson, Richard H., John K. Salmon, Ron O. Dror, et al.. (2008). High-throughput pairwise point interactions in Anton, a specialized machine for molecular dynamics simulation. 331–342. 15 indexed citations
9.
Maragakis, Paul, Kresten Lindorff‐Larsen, Michael P. Eastwood, et al.. (2008). Microsecond Molecular Dynamics Simulation Shows Effect of Slow Loop Dynamics on Backbone Amide Order Parameters of Proteins. The Journal of Physical Chemistry B. 112(19). 6155–6158. 163 indexed citations
10.
Arkin, Isaiah T., Huafeng Xu, Morten Ø. Jensen, et al.. (2007). Mechanism of Na + /H + Antiporting. Science. 317(5839). 799–803. 124 indexed citations
11.
Bowers, K. J., Federico D. Sacerdoti, John K. Salmon, et al.. (2006). Molecular dynamics---Scalable algorithms for molecular dynamics simulations on commodity clusters. 84–84. 1925 indexed citations breakdown →
12.
McAllister, S. R., et al.. (2006). Novel approach for α‐helical topology prediction in globular proteins: Generation of interhelical restraints. Proteins Structure Function and Bioinformatics. 65(4). 930–952. 15 indexed citations
13.
Shan, Yibing, John L. Klepeis, Michael P. Eastwood, Ron O. Dror, & David E. Shaw. (2005). Gaussian split Ewald: A fast Ewald mesh method for molecular simulation. The Journal of Chemical Physics. 122(5). 54101–54101. 323 indexed citations
14.
Klepeis, John L., Yinan Wei, Michael H. Hecht, & Christodoulos A. Floudas. (2004). Ab initio prediction of the three‐dimensional structure of a de novo designed protein: A double‐blind case study. Proteins Structure Function and Bioinformatics. 58(3). 560–570. 44 indexed citations
15.
Klepeis, John L., et al.. (2004). Design of Peptide Analogues with Improved Activity Using a Novel de Novo Protein Design Approach. Industrial & Engineering Chemistry Research. 43(14). 3817–3826. 44 indexed citations
16.
Klepeis, John L. & Christodoulos A. Floudas. (2003). ASTRO-FOLD: A Combinatorial and Global Optimization Framework for Ab Initio Prediction of Three-Dimensional Structures of Proteins from the Amino Acid Sequence. Biophysical Journal. 85(4). 2119–2146. 92 indexed citations
17.
Klepeis, John L., et al.. (2003). A new pairwise folding potential based on improved decoy generation and side‐chain packing. Proteins Structure Function and Bioinformatics. 54(2). 303–314. 36 indexed citations
18.
Klepeis, John L., et al.. (2003). Hybrid Global Optimization Algorithms for Protein Structure Prediction: Alternating Hybrids. Biophysical Journal. 84(2). 869–882. 53 indexed citations
19.
Klepeis, John L. & Christodoulos A. Floudas. (2000). Deterministic global optimization and torsion angle dynamics for molecular structure prediction. Computers & Chemical Engineering. 24(2-7). 1761–1766. 9 indexed citations
20.
Klepeis, John L., Marianthi Ierapetritou, & Christodoulos A. Floudas. (1998). Protein folding and peptide docking: A molecular modeling and global optimization approach. Computers & Chemical Engineering. 22. S3–S10. 12 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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