David E. Shaw
Impact in
- Computational Theory and Mathematics top 0.01%
- Computational Drug Discovery Methods
- Molecular Biology top 0.02%
- Protein Structure and Dynamics
- Receptor Mechanisms and Signaling
- RNA and protein synthesis mechanisms
- Lipid Membrane Structure and Behavior
Papers in
-
- Protein Structure and Dynamics 50
- Receptor Mechanisms and Signaling 25
- RNA and protein synthesis mechanisms 19
-
- Enzyme Structure and Function 28
- Co-authors
- Ron O. Dror (74 shared papers)Stefano Piana (21 shared papers)Kresten Lindorff‐Larsen (12 shared papers)John L. Klepeis (12 shared papers)Paul Maragakis (17 shared papers)Richard A. Friesner (4 shared papers)Yibing Shan (35 shared papers)Michael P. Eastwood (27 shared papers)
- Journals
- Proceedings of the National Academy of Sciences (19 papers)Biophysical Journal (15 papers)Journal of Chemical Theory and Computation (8 papers)Science (7 papers)Journal of the American Chemical Society (6 papers)
- Partner nations
- United StatesUnited KingdomAustralia
In The Last Decade
David E. Shaw
273 papers receiving 42.8k citations
David E. Shaw's Hit Papers
Peers
Comparison fields: 5 of 214
- Computational Theory and Mathematics 7.8k
- Molecular Biology 29.5k
- Cellular and Molecular Neuroscience 4.4k
- Spectroscopy 3.3k
- Oncology 4.2k
Countries citing papers authored by David E. Shaw
This map shows the geographic impact of David E. Shaw'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 David E. Shaw with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David E. Shaw more than expected).
Fields of papers citing papers by David E. Shaw
This network shows the impact of papers produced by David E. Shaw. 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 David E. Shaw. The network helps show where David E. Shaw may publish in the future.
Co-authors
The 25 scholars most cited alongside David E. Shaw, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 282 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Glide: A New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy Hit paper breakdown → | 2004 | 7559 |
| 2 | Improved side‐chain torsion potentials for the Amber ff99SB protein force field Hit paper breakdown → | 2010 | 4652 |
| 3 | Molecular dynamics---Scalable algorithms for molecular dynamics simulations on commodity clusters Hit paper breakdown → | 2006 | 1925 |
| 4 | A hierarchical approach to all‐atom protein loop prediction Hit paper breakdown → | 2004 | 1915 |
| 5 | How Fast-Folding Proteins Fold Hit paper breakdown → | 2011 | 1458 |
| 6 | Atomic-Level Characterization of the Structural Dynamics of Proteins Hit paper breakdown → | 2010 | 1415 |
| 7 | Scalable Algorithms for Molecular Dynamics Simulations on Commodity Clusters Hit paper breakdown → | 2006 | 1182 |
| 8 | PHASE: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results Hit paper breakdown → | 2006 | 947 |
| 9 | Biomolecular Simulation: A Computational Microscope for Molecular Biology Hit paper breakdown → | 2012 | 856 |
| 10 | Developing a molecular dynamics force field for both folded and disordered protein states Hit paper breakdown → | 2018 | 736 |
| 11 | Structure and dynamics of the M3 muscarinic acetylcholine receptor Hit paper breakdown → | 2012 | 648 |
| 12 | The Dynamic Process of β2-Adrenergic Receptor Activation Hit paper breakdown → | 2013 | 646 |
| 13 | Structure and function of an irreversible agonist-β2 adrenoceptor complex Hit paper breakdown → | 2011 | 643 |
| 14 | Water Dispersion Interactions Strongly Influence Simulated Structural Properties of Disordered Protein States Hit paper breakdown → | 2015 | 626 |
| 15 | Pathway and mechanism of drug binding to G-protein-coupled receptors Hit paper breakdown → | 2011 | 594 |
| 16 | Long-timescale molecular dynamics simulations of protein structure and function Hit paper breakdown → | 2009 | 578 |
| 17 | Systematic Validation of Protein Force Fields against Experimental Data Hit paper breakdown → | 2012 | 538 |
| 18 | How Does a Drug Molecule Find Its Target Binding Site? Hit paper breakdown → | 2011 | 484 |
| 19 | Activation mechanism of theβ2-adrenergic receptor Hit paper breakdown → | 2011 | 484 |
| 20 | 2012 | 458 |
About David E. Shaw
David E. Shaw is a scholar working on Molecular Biology, Materials Chemistry, Oncology, Cellular and Molecular Neuroscience and Spectroscopy, having authored 282 papers that have together received 43.5k indexed citations. Recurring topics across this work include Protein Structure and Dynamics (50 papers), Enzyme Structure and Function (28 papers), Receptor Mechanisms and Signaling (25 papers), Parallel Computing and Optimization Techniques (23 papers), RNA and protein synthesis mechanisms (19 papers), Monoclonal and Polyclonal Antibodies Research (18 papers), Neuropeptides and Animal Physiology (17 papers) and Advanced Data Storage Technologies (15 papers). The work is most often cited by research in Computational Theory and Mathematics (7.8k citations), Molecular Biology (29.5k citations), Cellular and Molecular Neuroscience (4.4k citations), Spectroscopy (3.3k citations) and Oncology (4.2k citations). David E. Shaw has collaborated with scholars based in United States, United Kingdom and Australia. Frequent co-authors include Ron O. Dror, Stefano Piana, Kresten Lindorff‐Larsen, John L. Klepeis, Paul Maragakis, Richard A. Friesner, Yibing Shan, Michael P. Eastwood, Huafeng Xu and Kim Palmö. Their work appears in journals such as Proceedings of the National Academy of Sciences, Biophysical Journal, Journal of Chemical Theory and Computation, Science and Journal of the American Chemical Society.
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.