Youngshang Pak

1.4k total citations
65 papers, 1.2k citations indexed

About

Youngshang Pak is a scholar working on Molecular Biology, Atomic and Molecular Physics, and Optics and Materials Chemistry. According to data from OpenAlex, Youngshang Pak has authored 65 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Molecular Biology, 24 papers in Atomic and Molecular Physics, and Optics and 16 papers in Materials Chemistry. Recurrent topics in Youngshang Pak's work include Protein Structure and Dynamics (27 papers), DNA and Nucleic Acid Chemistry (18 papers) and Spectroscopy and Quantum Chemical Studies (15 papers). Youngshang Pak is often cited by papers focused on Protein Structure and Dynamics (27 papers), DNA and Nucleic Acid Chemistry (18 papers) and Spectroscopy and Quantum Chemical Studies (15 papers). Youngshang Pak collaborates with scholars based in South Korea and United States. Youngshang Pak's co-authors include Soonmin Jang, Seokmin Shin, R. Claude Woods, Eunae Kim, Eunae Kim, Shaomeng Wang, Manho Lim, Gregory A. Voth, Christopher J. Cramer and Young‐A Lee and has published in prestigious journals such as Journal of the American Chemical Society, Physical Review Letters and Nucleic Acids Research.

In The Last Decade

Youngshang Pak

62 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Youngshang Pak South Korea 22 708 345 340 219 145 65 1.2k
Turgut Baştuğ Australia 22 494 0.7× 611 1.8× 256 0.8× 169 0.8× 162 1.1× 69 1.3k
David C. Chatfield United States 18 486 0.7× 590 1.7× 201 0.6× 295 1.3× 74 0.5× 34 1.2k
László Füsti-Molnár United States 14 353 0.5× 399 1.2× 151 0.4× 179 0.8× 73 0.5× 20 880
Judit E. Šponer Czechia 29 1.4k 2.0× 245 0.7× 298 0.9× 192 0.9× 44 0.3× 85 2.2k
E. W. Knapp Germany 20 1.0k 1.4× 660 1.9× 507 1.5× 172 0.8× 124 0.9× 43 1.7k
Ramón Crehuet Spain 27 970 1.4× 340 1.0× 482 1.4× 254 1.2× 94 0.6× 66 1.9k
Denis Bucher United States 21 1.0k 1.4× 337 1.0× 356 1.0× 235 1.1× 45 0.3× 31 1.6k
R. D. Levine Israel 19 711 1.0× 331 1.0× 305 0.9× 368 1.7× 20 0.1× 31 1.5k
Jürgen Schlitter Germany 22 1.2k 1.7× 449 1.3× 364 1.1× 228 1.0× 26 0.2× 46 1.7k
Giancarlo Baldini Italy 23 625 0.9× 998 2.9× 713 2.1× 186 0.8× 193 1.3× 85 2.3k

Countries citing papers authored by Youngshang Pak

Since Specialization
Citations

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

Fields of papers citing papers by Youngshang Pak

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Youngshang Pak

This figure shows the co-authorship network connecting the top 25 collaborators of Youngshang Pak. A scholar is included among the top collaborators of Youngshang Pak 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 Youngshang Pak. Youngshang Pak 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.
Pak, Youngshang, et al.. (2025). Ensemble-Based Precision Refinement of All-Atom Nucleic Acid Force Fields Guided by NMR NOE Pair-Distance Measurements. Journal of Chemical Theory and Computation. 21(21). 11245–11258.
2.
Pak, Youngshang, et al.. (2024). Free Energy-Based Refinement of DNA Force Field via Modification of Multiple Nonbonding Energy Terms. Journal of Chemical Information and Modeling. 65(1). 288–297. 1 indexed citations
3.
Pak, Youngshang, et al.. (2024). Three-State Diffusion Model of DNA Glycosylase Translocation along Stretched DNA as Revealed by Free Energy Landscapes at the All-Atom Level. Journal of Chemical Theory and Computation. 20(6). 2666–2675.
4.
5.
Jang, Soonmin, et al.. (2020). Computational Probing of Temperature-Dependent Unfolding of a Small Globular Protein: From Cold to Heat Denaturation. Journal of Chemical Theory and Computation. 17(1). 515–524. 7 indexed citations
6.
Pak, Youngshang, et al.. (2019). Complete photodissociation dynamics of CF2I2in solution. Physical Chemistry Chemical Physics. 21(13). 6859–6867. 11 indexed citations
7.
Kulkarni, Mandar, et al.. (2017). In silico direct folding of thrombin-binding aptamer G-quadruplex at all-atom level. Nucleic Acids Research. 45(22). 12648–12656. 37 indexed citations
8.
Kim, Eunae, et al.. (2015). Free energy landscape and transition pathways from Watson–Crick to Hoogsteen base pairing in free duplex DNA. Nucleic Acids Research. 43(16). 7769–7778. 35 indexed citations
9.
Pak, Youngshang, Ira Pastan, Robert J. Kreitman, & Byung Kook Lee. (2014). Effect of Antigen Shedding on Targeted Delivery of Immunotoxins in Solid Tumors from a Mathematical Model. PLoS ONE. 9(10). e110716–e110716. 15 indexed citations
10.
Jang, Soonmin, et al.. (2014). A fully atomistic computer simulation study of cold denaturation of a β-hairpin. Nature Communications. 5(1). 5773–5773. 41 indexed citations
11.
Pak, Youngshang, et al.. (2012). Antigen Shedding May Improve Efficiencies for Delivery of Antibody-Based Anticancer Agents in Solid Tumors. Cancer Research. 72(13). 3143–3152. 28 indexed citations
12.
Chang, Ziwei, Ming Lu, Hyun‐Kyung Park, et al.. (2012). Functional HSF1 Requires Aromatic-Participant Interactions in Protecting Mouse Embryonic Fibroblasts against Apoptosis Via G2 Cell Cycle Arrest. Molecules and Cells. 33(5). 465–470. 8 indexed citations
13.
Kim, Eunae, et al.. (2012). Potential of Mean Force Simulation by Pulling a DNA Aptamer in Complex with Thrombin. Bulletin of the Korean Chemical Society. 33(11). 3597–3600. 6 indexed citations
14.
Jang, Soonmin, et al.. (2011). Multiple stepwise pattern for potential of mean force in unfolding the thrombin binding aptamer in complex with Sr2+. The Journal of Chemical Physics. 135(22). 225104–225104. 17 indexed citations
15.
Pak, Youngshang, Jung‐Ho Shin, & Soonmin Jang. (2009). Computational Study of Human Calcitonin (hCT) Oligomer. Bulletin of the Korean Chemical Society. 30(12). 3006–3010. 2 indexed citations
16.
Kim, Eunae, Soonmin Jang, & Youngshang Pak. (2007). Consistent free energy landscapes and thermodynamic properties of small proteins based on a single all-atom force field employing an implicit solvation. The Journal of Chemical Physics. 127(14). 26 indexed citations
17.
Jang, Soonmin, Eunae Kim, & Youngshang Pak. (2005). Free energy surfaces of miniproteins with a ββα motif: Replica exchange molecular dynamics simulation with an implicit solvation model. Proteins Structure Function and Bioinformatics. 62(3). 663–671. 33 indexed citations
18.
Pak, Youngshang, Soonmin Jang, & Seokmin Shin. (2002). Prediction of helical peptide folding in an implicit water by a new molecular dynamics scheme with generalized effective potential. The Journal of Chemical Physics. 116(15). 6831–6835. 11 indexed citations
19.
Jang, Soonmin, Youngshang Pak, & Seokmin Shin. (2002). Multicanonical ensemble with Nosé–Hoover molecular dynamics simulation. The Journal of Chemical Physics. 116(12). 4782–4786. 15 indexed citations
20.
Pak, Youngshang & Shaomeng Wang. (1999). Application of a Molecular Dynamics Simulation Method with a Generalized Effective Potential to the Flexible Molecular Docking Problems. The Journal of Physical Chemistry B. 104(2). 354–359. 42 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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