N. Hung

402 total citations
19 papers, 227 citations indexed

About

N. Hung is a scholar working on Molecular Biology, Computational Theory and Mathematics and Cellular and Molecular Neuroscience. According to data from OpenAlex, N. Hung has authored 19 papers receiving a total of 227 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Molecular Biology, 9 papers in Computational Theory and Mathematics and 5 papers in Cellular and Molecular Neuroscience. Recurrent topics in N. Hung's work include Protein Structure and Dynamics (10 papers), Computational Drug Discovery Methods (9 papers) and Receptor Mechanisms and Signaling (8 papers). N. Hung is often cited by papers focused on Protein Structure and Dynamics (10 papers), Computational Drug Discovery Methods (9 papers) and Receptor Mechanisms and Signaling (8 papers). N. Hung collaborates with scholars based in United States, China and Russia. N. Hung's co-authors include Yinglong Miao, Jinan Wang, Apurba Bhattarai, Michael S. Wolfe, Xin‐Yun Huang, Allan Haldane, Ronald M. Levy, Keya Joshi, Rommie E. Amaro and Minfei Su and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and The Journal of Physical Chemistry B.

In The Last Decade

N. Hung

18 papers receiving 225 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
N. Hung United States 10 156 53 36 35 27 19 227
Henrik Keränen Sweden 10 313 2.0× 107 2.0× 50 1.4× 42 1.2× 42 1.6× 10 420
Maxime Louet France 12 340 2.2× 50 0.9× 19 0.5× 27 0.8× 108 4.0× 25 465
Harry Saavedra United States 6 304 1.9× 23 0.4× 66 1.8× 17 0.5× 23 0.9× 8 385
Lan Zhu United States 9 226 1.4× 23 0.4× 42 1.2× 17 0.5× 84 3.1× 18 285
Taku Yamashita Japan 12 189 1.2× 11 0.2× 16 0.4× 47 1.3× 17 0.6× 33 345
Steven A. Combs United States 7 244 1.6× 85 1.6× 69 1.9× 8 0.2× 35 1.3× 11 327
Anastasiia Gusach United States 7 128 0.8× 26 0.5× 21 0.6× 6 0.2× 48 1.8× 11 162
Matthew Holcomb United States 10 176 1.1× 36 0.7× 27 0.8× 6 0.2× 25 0.9× 21 263
Annie M. Westerlund Sweden 10 224 1.4× 48 0.9× 55 1.5× 5 0.1× 29 1.1× 17 309
Ícaro A. Simon Denmark 6 221 1.4× 36 0.7× 13 0.4× 18 0.5× 145 5.4× 8 300

Countries citing papers authored by N. Hung

Since Specialization
Citations

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

Fields of papers citing papers by N. Hung

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of N. Hung

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

All Works

19 of 19 papers shown
1.
Hung, N., Jessica Z. Kubicek-Sutherland, & S. Gnanakaran. (2025). Diverse toxins exhibit a common binding mode to the nicotinic acetylcholine receptors. Biophysical Journal. 124(8). 1195–1207.
2.
Hung, N., et al.. (2024). Understanding the impact of binding free energy and kinetics calculations in modern drug discovery. Expert Opinion on Drug Discovery. 19(6). 671–682. 17 indexed citations
3.
Lou, Jian-Shu, Minfei Su, Jinan Wang, et al.. (2024). Distinct binding conformations of epinephrine with α- and β-adrenergic receptors. Experimental & Molecular Medicine. 56(9). 1952–1966. 3 indexed citations
4.
Zhou, Rui, Masato Maesako, N. Hung, et al.. (2024). Familial Alzheimer mutations stabilize synaptotoxic γ-secretase-substrate complexes. Cell Reports. 43(2). 113761–113761. 15 indexed citations
5.
Wang, Jinan, et al.. (2024). PepBinding: A Workflow for Predicting Peptide Binding Structures by Combining Peptide Docking and Peptide Gaussian Accelerated Molecular Dynamics Simulations. The Journal of Physical Chemistry B. 128(30). 7332–7340. 3 indexed citations
6.
Yadav, Anju, N. Hung, Tyler Reddy, et al.. (2024). Insertion and Anchoring of the HIV-1 Fusion Peptide into a Complex Membrane Mimicking the Human T-Cell. The Journal of Physical Chemistry B. 128(51). 12710–12727. 1 indexed citations
7.
Su, Minfei, Jinan Wang, N. Hung, et al.. (2023). Structural basis of agonist specificity of α1A-adrenergic receptor. Nature Communications. 14(1). 4819–4819. 9 indexed citations
8.
Hung, N., Jinan Wang, & Yinglong Miao. (2023). Deep Learning Dynamic Allostery of G-Protein-Coupled Receptors. SHILAP Revista de lepidopterología. 3(11). 3165–3180. 25 indexed citations
9.
Wang, Jinan, et al.. (2023). Predicting Biomolecular Binding Kinetics: A Review. Journal of Chemical Theory and Computation. 19(8). 2135–2148. 35 indexed citations
10.
Hung, N., et al.. (2023). Effects of presenilin-1 familial Alzheimer’s disease mutations on γ-secretase activation for cleavage of amyloid precursor protein. Communications Biology. 6(1). 174–174. 17 indexed citations
11.
Joshi, Keya, et al.. (2023). Accelerating Molecular Dynamics Simulations for Drug Discovery. Methods in molecular biology. 2714. 187–202. 2 indexed citations
12.
Hung, N., et al.. (2023). Molecular Dynamics Activation of γ-Secretase for Cleavage of the Notch1 Substrate. ACS Chemical Neuroscience. 14(23). 4216–4226. 3 indexed citations
13.
Hung, N. & Yinglong Miao. (2023). Deep Boosted Molecular Dynamics: Accelerating Molecular Simulations with Gaussian Boost Potentials Generated Using Probabilistic Bayesian Deep Neural Network. The Journal of Physical Chemistry Letters. 14(21). 4970–4982. 7 indexed citations
14.
Hung, N., Lane Votapka, Keya Joshi, et al.. (2022). Gaussian Accelerated Molecular Dynamics in OpenMM. The Journal of Physical Chemistry B. 126(31). 5810–5820. 11 indexed citations
15.
Wang, Jinan, et al.. (2022). Molecular Simulations and Drug Discovery of Adenosine Receptors. Molecules. 27(7). 2054–2054. 10 indexed citations
16.
Hung, N., Jinan Wang, Apurba Bhattarai, & Yinglong Miao. (2022). GLOW: A Workflow Integrating Gaussian-Accelerated Molecular Dynamics and Deep Learning for Free Energy Profiling. Journal of Chemical Theory and Computation. 18(3). 1423–1436. 38 indexed citations
17.
Wang, Jinan, Apurba Bhattarai, N. Hung, & Yinglong Miao. (2022). Challenges and frontiers of computational modelling of biomolecular recognition. SHILAP Revista de lepidopterología. 3. 8 indexed citations
18.
Hung, N., et al.. (2021). Pathways and Mechanism of Caffeine Binding to Human Adenosine A2A Receptor. Frontiers in Molecular Biosciences. 8. 673170–673170. 17 indexed citations
19.
Hung, N., Allan Haldane, Ronald M. Levy, & Yinglong Miao. (2021). Unique features of different classes of G‐protein‐coupled receptors revealed from sequence coevolutionary and structural analysis. Proteins Structure Function and Bioinformatics. 90(2). 601–614. 6 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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