Hahnbeom Park

16.4k total citations · 9 hit papers
49 papers, 6.1k citations indexed

About

Hahnbeom Park is a scholar working on Molecular Biology, Materials Chemistry and Computational Theory and Mathematics. According to data from OpenAlex, Hahnbeom Park has authored 49 papers receiving a total of 6.1k indexed citations (citations by other indexed papers that have themselves been cited), including 45 papers in Molecular Biology, 32 papers in Materials Chemistry and 10 papers in Computational Theory and Mathematics. Recurrent topics in Hahnbeom Park's work include Protein Structure and Dynamics (39 papers), Enzyme Structure and Function (28 papers) and RNA and protein synthesis mechanisms (12 papers). Hahnbeom Park is often cited by papers focused on Protein Structure and Dynamics (39 papers), Enzyme Structure and Function (28 papers) and RNA and protein synthesis mechanisms (12 papers). Hahnbeom Park collaborates with scholars based in United States, South Korea and Puerto Rico. Hahnbeom Park's co-authors include Chaok Seok, Lim Heo, David Baker, Sergey Ovchinnikov, Ivan Anishchenko, Jin Hwan Ko, Frank DiMaio, Zhenling Peng, Jianyi Yang and David E. Kim and has published in prestigious journals such as Nature, Science and Proceedings of the National Academy of Sciences.

In The Last Decade

Hahnbeom Park

47 papers receiving 6.1k citations

Hit Papers

5 papers align trajectories

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.

GalaxyRefine: protein structure refinement driven by side-chain repacking

2013 948 citations Lim Heo, Hahnbeom Park et al. profile →
The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design

2017 942 citations Rebecca F. Alford, Andrew Leaver‐Fay et al. Journal of Chemical Theory and Computation profile →
Improved protein structure prediction using predicted interresidue orientations

2020 925 citations Jianyi Yang, Ivan Anishchenko et al. Proceedings of the National Academy of Sciences profile →
GalaxyWEB server for protein structure prediction and refinement

2012 673 citations Hahnbeom Park, Lim Heo et al. profile →
Simultaneous Optimization of Biomolecular Energy Functions on Features from Small Molecules and Macromolecules

2016 352 citations Hahnbeom Park, Philip Bradley et al. Journal of Chemical Theory and Computation profile →
Protein structure determination using metagenome sequence data

2017 350 citations Sergey Ovchinnikov, Hahnbeom Park et al. Science profile →
De novo design of a fluorescence-activating β-barrel

2018 249 citations Jiayi Dou, Anastassia A. Vorobieva et al. Nature profile →
Improved protein structure refinement guided by deep learning based accuracy estimation

2021 157 citations Naozumi Hiranuma, Hahnbeom Park et al. Nature Communications profile →
An artificial intelligence accelerated virtual screening platform for drug discovery

2024 69 citations Guangfeng Zhou, Domnița-Valeria Rusnac et al. Nature Communications profile →

Peers

Countries citing papers authored by Hahnbeom Park

Since Specialization

Citations

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

Fields of papers citing papers by Hahnbeom Park

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hahnbeom Park

This figure shows the co-authorship network connecting the top 25 collaborators of Hahnbeom Park. A scholar is included among the top collaborators of Hahnbeom Park 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 Hahnbeom Park. Hahnbeom Park 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

#	Work	Indexed citations
1	Artificial intelligence-driven discovery of novel scaffolds for selective TLR7 antagonists and their application in enhancing mRNA translation efficiency 2025 ·European Journal of Pharmaceutical Sciences ·(unknown), Keejung Yoon, (unknown), Gyochang Keum, Hahnbeom Park, Eun‐Kyoung Bang	0
2	Deep learning methods for proteome-scale interaction prediction 2025 ·Current Opinion in Structural Biology ·(unknown), Byeong‐Soo Bae, Kun Hee Kim, Hahnbeom Park, Minkyung Baek	1
3	Deep learning molecular interaction motifs from receptor structures alone 2025 ·Journal of Cheminformatics ·Seeun Kim, (unknown), Hyeonuk Woo, (unknown), Chaok Seok, Hahnbeom Park	0
4	Protein Ensemble Generation Through Variational Autoencoder Latent Space Sampling 2024 ·Journal of Chemical Theory and Computation ·(unknown), Minkyung Baek, Hahnbeom Park, Gyu Rie Lee, David Baker	19
5	An artificial intelligence accelerated virtual screening platform for drug discovery breakdown → 2024 ·Nature Communications ·Guangfeng Zhou, Domnița-Valeria Rusnac, Hahnbeom Park, (unknown), Hai M. Nguyen, Lance Stewart, Matthew F. Bush, Phuong T. Nguyen, Heike Wulff, Vladimir Yarov‐Yarovoy, Ning Zheng, Frank DiMaio	69
6	Understanding the molecular mechanisms of odorant binding and activation of the human OR52 family 2023 ·Nature Communications ·(unknown), (unknown), Seonghan Kim, (unknown), Hyunook Kang, Jinuk Kim, Injin Bang, (unknown), Won‐Ki Huh, Chaok Seok, Hahnbeom Park, Wonpil Im, Hee‐Jung Choi	22
7	Development of a novel histone deacetylase inhibitor unveils the role of HDAC11 in alleviating depression by inhibition of microglial activation 2023 ·Biomedicine & Pharmacotherapy ·(unknown), (unknown), (unknown), Kyungmin Lee, (unknown), Hahnbeom Park, Minseob Koh, Eunha Kim, Michael E. Jung, Dimitrios Iliopoulos, Jeong‐Yeon Lee, Jonghoon Kim, Sanghee Lee	17
8	Improved protein structure refinement guided by deep learning based accuracy estimation breakdown → 2021 ·Nature Communications ·Naozumi Hiranuma, Hahnbeom Park, Minkyung Baek, Ivan Anishchenko, Justas Dauparas, David Baker	157
9	Divergent acyl carrier protein decouples mitochondrial Fe-S cluster biogenesis from fatty acid synthesis in malaria parasites 2021 ·eLife ·(unknown), (unknown), Yasaman Jami‐Alahmadi, Hahnbeom Park, James A. Wohlschlegel, Paul A. Sigala	11
10	Improved protein structure prediction using predicted interresidue orientations breakdown → 2020 ·Proceedings of the National Academy of Sciences ·Jianyi Yang, Ivan Anishchenko, Hahnbeom Park, Zhenling Peng, Sergey Ovchinnikov, David Baker	925
11	Prediction of Protein Mutational Free Energy: Benchmark and Sampling Improvements Increase Classification Accuracy 2020 ·Frontiers in Bioengineering and Biotechnology ·Brandon Frenz, Steven M. Lewis, Chris King, Frank DiMaio, Hahnbeom Park, Yifan Song	62
12	Protein homology model refinement by large-scale energy optimization 2018 ·Proceedings of the National Academy of Sciences ·Hahnbeom Park, Sergey Ovchinnikov, David E. Kim, Frank DiMaio, David Baker	47
13	De novo design of a fluorescence-activating β-barrel breakdown → 2018 ·Nature ·Jiayi Dou, Anastassia A. Vorobieva, William Sheffler, Lindsey Doyle, Hahnbeom Park, Matthew J. Bick, Binchen Mao, Glenna Foight, Min Yen Lee, Lauren Gagnon, Lauren Carter, Banumathi Sankaran, Sergey Ovchinnikov, Enrique Marcos, Po‐Ssu Huang, Joshua C. Vaughan, Barry Stoddard, David Baker	249
14	Protein structure determination using metagenome sequence data breakdown → 2017 ·Science ·Sergey Ovchinnikov, Hahnbeom Park, Neha Varghese, Po‐Ssu Huang, Georgios A. Pavlopoulos, David E. Kim, Hetunandan Kamisetty, Nikos C. Kyrpides, David Baker	350
15	The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design breakdown → 2017 ·Journal of Chemical Theory and Computation ·Rebecca F. Alford, Andrew Leaver‐Fay, Jeliazko R. Jeliazkov, Matthew J. O’Meara, Frank DiMaio, Hahnbeom Park, Maxim V. Shapovalov, P. Douglas Renfrew, Vikram Khipple Mulligan, Kalli Kappel, Jason W. Labonte, Michael S. Pacella, Richard Bonneau, Philip Bradley, Roland L. Dunbrack, Rhiju Das, David Baker, Brian Kuhlman, Tanja Kortemme, Jeffrey J. Gray	942
16	Structure prediction using sparse simulated NOE restraints with Rosetta in CASP 11 2016 ·Proteins Structure Function and Bioinformatics ·Sergey Ovchinnikov, Hahnbeom Park, David E. Kim, Yuan Liu, Ray Yu‐Ruei Wang, David Baker	15
17	The Origin of Consistent Protein Structure Refinement from Structural Averaging 2015 ·Structure ·Hahnbeom Park, Frank DiMaio, David Baker	17
18	High-resolution protein–protein docking by global optimization: recent advances and future challenges 2015 ·Current Opinion in Structural Biology ·Hahnbeom Park, Hasup Lee, Chaok Seok	33
19	Protein Loop Modeling Using a New Hybrid Energy Function and Its Application to Modeling in Inaccurate Structural Environments 2014 ·PLoS ONE ·Hahnbeom Park, Gyu Rie Lee, Lim Heo, Chaok Seok	70
20	GalaxyTBM: template-based modeling by building a reliable core and refining unreliable local regions 2012 ·BMC Bioinformatics ·Junsu Ko, Hahnbeom Park, Chaok Seok	82

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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