Darrin M. York

55.2k total citations · 4 hit papers
251 papers, 35.4k citations indexed

About

Darrin M. York is a scholar working on Molecular Biology, Atomic and Molecular Physics, and Optics and Materials Chemistry. According to data from OpenAlex, Darrin M. York has authored 251 papers receiving a total of 35.4k indexed citations (citations by other indexed papers that have themselves been cited), including 162 papers in Molecular Biology, 68 papers in Atomic and Molecular Physics, and Optics and 37 papers in Materials Chemistry. Recurrent topics in Darrin M. York's work include DNA and Nucleic Acid Chemistry (84 papers), RNA and protein synthesis mechanisms (74 papers) and Protein Structure and Dynamics (67 papers). Darrin M. York is often cited by papers focused on DNA and Nucleic Acid Chemistry (84 papers), RNA and protein synthesis mechanisms (74 papers) and Protein Structure and Dynamics (67 papers). Darrin M. York collaborates with scholars based in United States, Canada and Spain. Darrin M. York's co-authors include Lee G. Pedersen, Tom Darden, Timothy J. Giese, Tai‐Sung Lee, Weitao Yang, Martin Karplus, George M. Giambaşu, Jiali Gao, Kwangho Nam and Xabier López and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Journal of the American Chemical Society.

In The Last Decade

Darrin M. York

248 papers receiving 34.9k citations

Hit Papers

align trajectories sort by overperformance

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.

Particle mesh Ewald: An N⋅log(N) method for Ewald sums in large systems

1993 25.0k citations Darrin M. York et al. The Journal of Chemical Physics profile →
The effect of long-range electrostatic interactions in simulations of macromolecular crystals: A comparison of the Ewald and truncated list methods

1993 609 citations Darrin M. York et al. The Journal of Chemical Physics profile →
GPU-Accelerated Molecular Dynamics and Free Energy Methods in Amber18: Performance Enhancements and New Features

2018 330 citations Tai‐Sung Lee, Charles Lin et al. Journal of Chemical Information and Modeling profile →
Alchemical Binding Free Energy Calculations in AMBER20: Advances and Best Practices for Drug Discovery

2020 247 citations Tai‐Sung Lee, Bryce K. Allen et al. Journal of Chemical Information and Modeling profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Darrin M. York United States	52	20.8k	6.2k	6.0k	4.0k	3.1k	251	35.4k
Christopher I. Bayly United States	33	17.5k 0.8×	5.8k 0.9×	4.8k 0.8×	4.3k 1.1×	2.0k 0.7×	77	29.0k
Piotr Cieplak United States	46	26.4k 1.3×	7.3k 1.2×	6.7k 1.1×	4.8k 1.2×	2.5k 0.8×	126	41.0k
Alan E. Mark Australia	74	26.8k 1.3×	8.1k 1.3×	7.6k 1.3×	5.6k 1.4×	4.7k 1.5×	242	44.1k
Bernard R. Brooks United States	66	24.1k 1.2×	8.1k 1.3×	8.5k 1.4×	3.0k 0.7×	2.6k 0.8×	307	35.5k
Julian Tirado‐Rives United States	46	12.9k 0.6×	7.5k 1.2×	6.1k 1.0×	5.2k 1.3×	3.9k 1.3×	100	31.2k
Jeffry D. Madura United States	37	24.2k 1.2×	8.3k 1.3×	9.7k 1.6×	4.6k 1.1×	5.5k 1.8×	127	43.4k
Roger Impey Canada	19	20.6k 1.0×	6.9k 1.1×	8.8k 1.5×	3.5k 0.9×	4.1k 1.3×	42	36.5k
James W. Caldwell United States	22	21.0k 1.0×	7.3k 1.2×	6.2k 1.0×	4.7k 1.2×	2.8k 0.9×	33	36.1k
Giovanni Ciccotti Italy	52	14.1k 0.7×	6.9k 1.1×	8.6k 1.4×	2.5k 0.6×	3.4k 1.1×	220	29.4k
Thomas E. Cheatham United States	61	29.0k 1.4×	4.9k 0.8×	3.6k 0.6×	3.3k 0.8×	2.0k 0.7×	185	39.1k

Countries citing papers authored by Darrin M. York

Since Specialization

Citations

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

Fields of papers citing papers by Darrin M. York

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Darrin M. York. 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 Darrin M. York. The network helps show where Darrin M. York may publish in the future.

Co-authorship network of co-authors of Darrin M. York

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

All Works

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20 of 20 papers shown

Giese, Timothy J., et al.. (2025). FE-ToolKit: A Versatile Software Suite for Analysis of High-Dimensional Free Energy Surfaces and Alchemical Free Energy Networks. Journal of Chemical Information and Modeling. 65(11). 5273–5279.

Giese, Timothy J., et al.. (2024). Surface-Accelerated String Method for Locating Minimum Free Energy Paths. Journal of Chemical Theory and Computation. 20(5). 2058–2073. 8 indexed citations

Tao, Yujun, Timothy J. Giese, Şölen Ekesan, et al.. (2024). Amber free energy tools: Interoperable software for free energy simulations using generalized quantum mechanical/molecular mechanical and machine learning potentials. The Journal of Chemical Physics. 160(22). 10 indexed citations

Giese, Timothy J., et al.. (2024). Software Infrastructure for Next-Generation QM/MM−ΔMLP Force Fields. The Journal of Physical Chemistry B. 128(26). 6257–6271. 15 indexed citations

Tao, Yujun, Timothy J. Giese, & Darrin M. York. (2024). Electronic and Nuclear Quantum Effects on Proton Transfer Reactions of Guanine–Thymine (G-T) Mispairs Using Combined Quantum Mechanical/Molecular Mechanical and Machine Learning Potentials. Molecules. 29(11). 2703–2703. 3 indexed citations

Wilson, Timothy J., Şölen Ekesan, Timothy J. Giese, et al.. (2024). The Role of General Acid Catalysis in the Mechanism of an Alkyl Transferase Ribozyme. ACS Catalysis. 14(20). 15294–15305. 5 indexed citations

Zeng, Jinzhe, Yujun Tao, Timothy J. Giese, & Darrin M. York. (2023). QDπ: A Quantum Deep Potential Interaction Model for Drug Discovery. Journal of Chemical Theory and Computation. 19(4). 1261–1275. 30 indexed citations

Ekesan, Şölen, Timothy J. Giese, Timothy J. Wilson, et al.. (2023). Catalytic mechanism and pH dependence of a methyltransferase ribozyme (MTR1) from computational enzymology. Nucleic Acids Research. 51(9). 4508–4518. 9 indexed citations

Zeng, Jinzhe, Yujun Tao, Timothy J. Giese, & Darrin M. York. (2023). Modern semiempirical electronic structure methods and machine learning potentials for drug discovery: Conformers, tautomers, and protonation states. The Journal of Chemical Physics. 158(12). 124110–124110. 17 indexed citations

10.

Oldemeyer, Sabine, et al.. (2021). Peripheral Methionine Residues Impact Flavin Photoreduction and Protonation in an Engineered LOV Domain Light Sensor. Biochemistry. 60(15). 1148–1164. 6 indexed citations

11.

Lee, Tai‐Sung, Zhixiong Lin, Bryce K. Allen, et al.. (2020). Improved Alchemical Free Energy Calculations with Optimized Smoothstep Softcore Potentials. Journal of Chemical Theory and Computation. 16(9). 5512–5525. 44 indexed citations

12.

Lee, Tai‐Sung, Bryce K. Allen, Timothy J. Giese, et al.. (2020). Alchemical Binding Free Energy Calculations in AMBER20: Advances and Best Practices for Drug Discovery. Journal of Chemical Information and Modeling. 60(11). 5595–5623. 247 indexed citations breakdown →

13.

Tsai, Hsu‐Chun, Yujun Tao, Tai‐Sung Lee, Kenneth M. Merz, & Darrin M. York. (2020). Validation of Free Energy Methods in AMBER. Journal of Chemical Information and Modeling. 60(11). 5296–5300. 22 indexed citations

14.

York, Darrin M., et al.. (2019). Molecular simulations of the pistol ribozyme: unifying the interpretation of experimental data and establishing functional links with the hammerhead ribozyme. RNA. 25(11). 1439–1456. 21 indexed citations

15.

Giambaşu, George M., et al.. (2015). Force Field for Mg2+, Mn2+, Zn2+, and Cd2+ Ions That Have Balanced Interactions with Nucleic Acids. The Journal of Physical Chemistry. 15 indexed citations

16.

Gu, Hong, Shuming Zhang, K. Y. Wong, et al.. (2013). Experimental and computational analysis of the transition state for ribonuclease A-catalyzed RNA 2′- O -transphosphorylation. Proceedings of the National Academy of Sciences. 110(32). 13002–13007. 59 indexed citations

17.

York, Darrin M. & Tai‐Sung Lee. (2009). Multi-scale quantum models for biocatalysis : modern techniques and applications. Springer eBooks. 17 indexed citations

18.

Moser, Adam, Rebecca Guza, Natalia Tretyakova, & Darrin M. York. (2008). Density functional study of the influence of C5 cytosine substitution in base pairs with guanine. Theoretical Chemistry Accounts. 122(3-4). 179–188. 13 indexed citations

19.

Hildebrand, A. R., Háttula Moholy-Nagy, Christian Koeberl, et al.. (1994). Tektites Found in the Ruins of the Maya City of Tikal, Guatemala. LPI. 549. 4 indexed citations

20.

Bottomley, R. J., Darrin M. York, & R. A. F. Grieve. (1990). 40 Argon- 39 Argon dating of impact craters.. Lunar and Planetary Science Conference Proceedings. 20. 421–431. 25 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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