Jay W. Ponder

15.9k total citations · 9 hit papers
86 papers, 12.1k citations indexed

About

Jay W. Ponder is a scholar working on Molecular Biology, Atomic and Molecular Physics, and Optics and Spectroscopy. According to data from OpenAlex, Jay W. Ponder has authored 86 papers receiving a total of 12.1k indexed citations (citations by other indexed papers that have themselves been cited), including 51 papers in Molecular Biology, 41 papers in Atomic and Molecular Physics, and Optics and 20 papers in Spectroscopy. Recurrent topics in Jay W. Ponder's work include Protein Structure and Dynamics (39 papers), Spectroscopy and Quantum Chemical Studies (30 papers) and Advanced Chemical Physics Studies (20 papers). Jay W. Ponder is often cited by papers focused on Protein Structure and Dynamics (39 papers), Spectroscopy and Quantum Chemical Studies (30 papers) and Advanced Chemical Physics Studies (20 papers). Jay W. Ponder collaborates with scholars based in United States, France and Germany. Jay W. Ponder's co-authors include Pengyu Ren, Frederic M. Richards, David A. Case, Chuanjie Wu, Michael J. Schnieders, Teresa Head‐Gordon, Vijay S. Pande, Alan Grossfield, Jean‐Philip Piquemal and M. Dudek and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of the American Chemical Society and Journal of Biological Chemistry.

In The Last Decade

Jay W. Ponder

84 papers receiving 11.9k citations

Hit Papers

Force Fields for Protein Simulations 1987 2026 2000 2013 2003 1987 2003 2010 1987 400 800 1.2k
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. Force Fields for Protein Simulations
    2003 1.5k citations Jay W. Ponder, David A. Case Advances in protein chemistry profile →
  2. Tertiary templates for proteins
    1987 1.3k citations Jay W. Ponder et al. Journal of Molecular Biology profile →
  3. Polarizable Atomic Multipole Water Model for Molecular Mechanics Simulation
    2003 1.2k citations Pengyu Ren, Jay W. Ponder The Journal of Physical Chemistry B profile →
  4. Current Status of the AMOEBA Polarizable Force Field
    2010 1.1k citations Jay W. Ponder, Pengyu Ren et al. The Journal of Physical Chemistry B profile →
  5. An efficient newton‐like method for molecular mechanics energy minimization of large molecules
    1987 792 citations Jay W. Ponder et al. Journal of Computational Chemistry profile →
  6. Polarizable Atomic Multipole-Based AMOEBA Force Field for Proteins
    2013 523 citations Jay W. Ponder, Pengyu Ren et al. profile →
  7. Tinker 8: Software Tools for Molecular Design
    2018 488 citations Joshua A. Rackers, Zhi Wang et al. profile →
  8. Ion Solvation Thermodynamics from Simulation with a Polarizable Force Field
    2003 449 citations Pengyu Ren, Jay W. Ponder et al. profile →
  9. Polarizable Atomic Multipole-Based Molecular Mechanics for Organic Molecules
    2011 378 citations Pengyu Ren, Jay W. Ponder et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jay W. Ponder United States 41 6.6k 4.6k 3.3k 1.8k 1.5k 86 12.1k
Pengyu Ren United States 50 5.7k 0.9× 4.9k 1.1× 3.4k 1.0× 1.7k 1.0× 1.6k 1.1× 180 12.8k
Qiang Cui United States 72 8.9k 1.4× 4.5k 1.0× 3.7k 1.1× 1.7k 1.0× 1.5k 1.0× 394 16.5k
Thomas Fox Germany 28 9.2k 1.4× 3.0k 0.7× 3.4k 1.0× 1.8k 1.0× 1.4k 0.9× 56 15.1k
Teresa Head‐Gordon United States 57 6.1k 0.9× 6.0k 1.3× 5.0k 1.5× 2.4k 1.3× 1.6k 1.1× 243 14.7k
Ronald M. Levy United States 64 8.2k 1.2× 3.7k 0.8× 2.7k 0.8× 1.8k 1.0× 1.2k 0.8× 232 12.6k
B. Montgomery Pettitt United States 62 7.5k 1.1× 5.2k 1.1× 2.9k 0.9× 1.6k 0.9× 1.8k 1.2× 287 13.0k
David C. Spellmeyer United States 26 8.3k 1.3× 2.9k 0.6× 3.0k 0.9× 1.7k 0.9× 1.3k 0.9× 43 14.9k
Angel E. Garcı́a United States 64 10.8k 1.6× 4.0k 0.9× 4.4k 1.3× 2.1k 1.2× 962 0.7× 184 14.4k
Ulf Ryde Sweden 69 9.2k 1.4× 2.9k 0.6× 4.5k 1.4× 1.2k 0.7× 1.5k 1.0× 315 18.6k
U. Chandra Singh United States 28 5.6k 0.8× 2.8k 0.6× 2.1k 0.6× 1.5k 0.8× 1.5k 1.0× 65 10.3k

Countries citing papers authored by Jay W. Ponder

Since Specialization
Citations

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

Fields of papers citing papers by Jay W. Ponder

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jay W. Ponder. 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 Jay W. Ponder. The network helps show where Jay W. Ponder may publish in the future.

Co-authorship network of co-authors of Jay W. Ponder

This figure shows the co-authorship network connecting the top 25 collaborators of Jay W. Ponder. A scholar is included among the top collaborators of Jay W. Ponder 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 Jay W. Ponder. Jay W. Ponder 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.
Ansari, Narjes, Chengwen Liu, Jérôme Hénin, et al.. (2025). Targeting RNA with small molecules using state-of-the-art methods provides highly predictive affinities of riboswitch inhibitors. Communications Biology. 8(1). 1405–1405.
2.
3.
Casavant, Thomas L., et al.. (2023). A generalized Kirkwood implicit solvent for the polarizable AMOEBA protein model. The Journal of Chemical Physics. 159(5). 2 indexed citations
4.
Wang, Zhi, et al.. (2022). Classical Exchange Polarization: An Anisotropic Variable Polarizability Model. The Journal of Physical Chemistry B. 126(39). 7579–7594. 9 indexed citations
5.
Laury, Marie L., et al.. (2020). AMOEBA binding free energies for the SAMPL7 TrimerTrip host–guest challenge. Journal of Computer-Aided Molecular Design. 35(1). 79–93. 22 indexed citations
6.
Dollins, D.E., Jian-Ping Xiong, David E. Anderson, et al.. (2020). A structural basis for lithium and substrate binding of an inositide phosphatase. Journal of Biological Chemistry. 296. 100059–100059. 10 indexed citations
7.
Jolly, Luc-Henri, Alejandro Durán, Louis Lagardère, et al.. (2019). Raising the Performance of the Tinker-HP Molecular Modeling Package [Article v1.0]. arXiv (Cornell University). 1(2). 10409–10409. 10 indexed citations
8.
Rackers, Joshua A. & Jay W. Ponder. (2019). Classical Pauli repulsion: An anisotropic, atomic multipole model. The Journal of Chemical Physics. 150(8). 84104–84104. 70 indexed citations
9.
Rackers, Joshua A., Chengwen Liu, Pengyu Ren, & Jay W. Ponder. (2018). A physically grounded damped dispersion model with particle mesh Ewald summation. The Journal of Chemical Physics. 149(8). 84115–84115. 18 indexed citations
10.
Laury, Marie L., et al.. (2018). Absolute binding free energies for the SAMPL6 cucurbit[8]uril host–guest challenge via the AMOEBA polarizable force field. Journal of Computer-Aided Molecular Design. 32(10). 1087–1095. 26 indexed citations
11.
Bell, David R., Rui Qi, Zhifeng Jing, et al.. (2016). Calculating binding free energies of host–guest systems using the AMOEBA polarizable force field. Physical Chemistry Chemical Physics. 18(44). 30261–30269. 37 indexed citations
12.
Pickard, Frank C., Gerhard König, Florentina Tofoleanu, et al.. (2016). Blind prediction of distribution in the SAMPL5 challenge with QM based protomer and pK a corrections. Journal of Computer-Aided Molecular Design. 30(11). 1087–1100. 26 indexed citations
13.
Kuster, Daniel J., Chengyu Liu, John Fang, Jay W. Ponder, & Garland R. Marshall. (2015). High-Resolution Crystal Structures of Protein Helices Reconciled with Three-Centered Hydrogen Bonds and Multipole Electrostatics. PLoS ONE. 10(4). e0123146–e0123146. 24 indexed citations
14.
Wang, Lee‐Ping, Teresa Head‐Gordon, Jay W. Ponder, et al.. (2014). Systematic Improvement on the Classical Molecular Model of Water. Biophysical Journal. 106(2). 403a–403a. 4 indexed citations
15.
Ponder, Jay W., et al.. (2012). A valence bond model for aqueous Cu(II) and Zn(II) ions in the AMOEBA polarizable force field. Journal of Computational Chemistry. 34(9). 739–749. 34 indexed citations
16.
Kuster, Daniel J., Sérgio M. Urahata, Jay W. Ponder, & Garland R. Marshall. (2009). From Data or Dogma? The Myth of the Ideal Helix. Biophysical Journal. 96(3). 5a–5a. 1 indexed citations
17.
Ponder, Jay W. & David A. Case. (2003). Force Fields for Protein Simulations. Advances in protein chemistry. 66. 27–85. 1478 indexed citations breakdown →
18.
Ren, Pengyu & Jay W. Ponder. (2002). Consistent treatment of inter‐ and intramolecular polarization in molecular mechanics calculations. Journal of Computational Chemistry. 23(16). 1497–1506. 480 indexed citations
19.
Lu, Jianyun, Changguo Tang, Jay W. Ponder, et al.. (2000). Binding of retinol induces changes in rat cellular retinol-binding protein II conformation and backbone dynamics. Journal of Molecular Biology. 300(3). 619–632. 34 indexed citations
20.
York, John D., et al.. (1994). Crystal Structure of Inositol Polyphosphate 1-Phosphatase at 2.3-.ANG. Resolution. Biochemistry. 33(45). 13164–13171. 46 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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