Minkyung Baek

12.8k total citations · 6 hit papers
44 papers, 1.6k citations indexed

About

Minkyung Baek is a scholar working on Molecular Biology, Materials Chemistry and Computational Theory and Mathematics. According to data from OpenAlex, Minkyung Baek has authored 44 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 39 papers in Molecular Biology, 16 papers in Materials Chemistry and 10 papers in Computational Theory and Mathematics. Recurrent topics in Minkyung Baek's work include Protein Structure and Dynamics (33 papers), Enzyme Structure and Function (15 papers) and RNA and protein synthesis mechanisms (12 papers). Minkyung Baek is often cited by papers focused on Protein Structure and Dynamics (33 papers), Enzyme Structure and Function (15 papers) and RNA and protein synthesis mechanisms (12 papers). Minkyung Baek collaborates with scholars based in United States, South Korea and Puerto Rico. Minkyung Baek's co-authors include David Baker, Chaok Seok, Ivan Anishchenko, Frank DiMaio, Justas Dauparas, Hahnbeom Park, Taeyong Park, Lim Heo, Naozumi Hiranuma and Ryan McHugh and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and Nucleic Acids Research.

In The Last Decade

Minkyung Baek

43 papers receiving 1.6k citations

Hit Papers

Accurate prediction of protein–nucleic acid complexe... 2021 2026 2022 2024 2023 2023 2021 2022 2023 50 100 150
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.

  1. Accurate prediction of protein–nucleic acid complexes using RoseTTAFoldNA
    2023 176 citations Minkyung Baek, Ivan Anishchenko et al. Nature Methods profile →
  2. Improving de novo protein binder design with deep learning
    2023 158 citations Nathaniel R. Bennett, Brian Coventry et al. Nature Communications profile →
  3. Improved protein structure refinement guided by deep learning based accuracy estimation
    2021 157 citations Naozumi Hiranuma, Hahnbeom Park et al. Nature Communications profile →
  4. Hallucinating symmetric protein assemblies
    2022 113 citations Basile I. M. Wicky, Lukas F. Milles et al. Science profile →
  5. Top-down design of protein architectures with reinforcement learning
    2023 76 citations Shunzhi Wang, Christoffer Norn et al. Science profile →
  6. Peptide-binding specificity prediction using fine-tuned protein structure prediction networks
    2023 63 citations Justas Dauparas, Minkyung Baek et al. Proceedings of the National Academy of Sciences profile →

Peers

Minkyung Baek
Philippe Roche France
Takashi Ishida Japan
David Gatchell United States
Domenico Cozzetto United Kingdom
Narcís Fernández‐Fuentes United Kingdom
Shandar Ahmad Japan
Kalli Kappel United States
P. Douglas Renfrew United States
Jiahua He China
Hetunandan Kamisetty United States
Philippe Roche France
Minkyung Baek
Citations per year, relative to Minkyung Baek Minkyung Baek (= 1×) peers Philippe Roche

Countries citing papers authored by Minkyung Baek

Since Specialization
Citations

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

Fields of papers citing papers by Minkyung Baek

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Minkyung Baek

This figure shows the co-authorship network connecting the top 25 collaborators of Minkyung Baek. A scholar is included among the top collaborators of Minkyung Baek 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 Minkyung Baek. Minkyung Baek 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.
Park, Sung‐Joon, et al.. (2025). Advancing protein structure prediction beyond AlphaFold2. Current Opinion in Structural Biology. 90. 102985–102985. 4 indexed citations
2.
Bae, Byeong‐Soo, et al.. (2025). Deep learning methods for proteome-scale interaction prediction. Current Opinion in Structural Biology. 90. 102981–102981. 1 indexed citations
3.
Sledzieski, Samuel, Meghana Kshirsagar, Minkyung Baek, et al.. (2024). Democratizing protein language models with parameter-efficient fine-tuning. Proceedings of the National Academy of Sciences. 121(26). e2405840121–e2405840121. 25 indexed citations
4.
Baek, Minkyung, et al.. (2024). Protein Ensemble Generation Through Variational Autoencoder Latent Space Sampling. Journal of Chemical Theory and Computation. 20(7). 2689–2695. 19 indexed citations
5.
Humphreys, Ian R., Jing Zhang, Minkyung Baek, et al.. (2024). Protein interactions in human pathogens revealed through deep learning. Nature Microbiology. 9(10). 2642–2652. 14 indexed citations
6.
Baek, Minkyung. (2024). Towards the prediction of general biomolecular interactions with AI. Nature Methods. 21(8). 1382–1383. 4 indexed citations
7.
Bennett, Nathaniel R., Brian Coventry, Inna Goreshnik, et al.. (2023). Improving de novo protein binder design with deep learning. Nature Communications. 14(1). 2625–2625. 158 indexed citations breakdown →
8.
Dauparas, Justas, et al.. (2023). Peptide-binding specificity prediction using fine-tuned protein structure prediction networks. Proceedings of the National Academy of Sciences. 120(9). e2216697120–e2216697120. 63 indexed citations breakdown →
9.
Nieweglowska, Eliza, Axel F. Brilot, Minkyung Baek, et al.. (2023). The ϕPA3 phage nucleus is enclosed by a self-assembling 2D crystalline lattice. Nature Communications. 14(1). 927–927. 20 indexed citations
10.
Baek, Minkyung, et al.. (2023). Zero‐shot mutation effect prediction on protein stability and function using RoseTTAFold. Protein Science. 32(11). e4780–e4780. 18 indexed citations
11.
Wang, Shunzhi, Christoffer Norn, Alexis Courbet, et al.. (2023). Top-down design of protein architectures with reinforcement learning. Science. 380(6642). 266–273. 76 indexed citations breakdown →
12.
Haase, Max A. B., et al.. (2023). Protein–protein interactions in the Mla lipid transport system probed by computational structure prediction and deep mutational scanning. Journal of Biological Chemistry. 299(6). 104744–104744. 15 indexed citations
13.
Wicky, Basile I. M., Lukas F. Milles, Alexis Courbet, et al.. (2022). Hallucinating symmetric protein assemblies. Science. 378(6615). 56–61. 113 indexed citations breakdown →
14.
Rathinaswamy, Manoj Kumar, Udit Dalwadi, Kaelin D. Fleming, et al.. (2021). Structure of the phosphoinositide 3-kinase (PI3K) p110γ-p101 complex reveals molecular mechanism of GPCR activation. Science Advances. 7(35). 32 indexed citations
15.
Hiranuma, Naozumi, Hahnbeom Park, Minkyung Baek, et al.. (2021). Improved protein structure refinement guided by deep learning based accuracy estimation. Nature Communications. 12(1). 1340–1340. 157 indexed citations breakdown →
16.
Baek, Minkyung, Taeyong Park, Lim Heo, & Chaok Seok. (2020). Modeling Protein Homo-Oligomer Structures with GalaxyHomomer Web Server. Methods in molecular biology. 2165. 127–137. 4 indexed citations
17.
Baek, Minkyung, Taeyong Park, Lim Heo, Chiwook Park, & Chaok Seok. (2017). GalaxyHomomer: a web server for protein homo-oligomer structure prediction from a monomer sequence or structure. Nucleic Acids Research. 45(W1). W320–W324. 93 indexed citations
18.
Lee, Juyong, Florentina Tofoleanu, Frank C. Pickard, et al.. (2016). Absolute binding free energy calculations of CBClip host–guest systems in the SAMPL5 blind challenge. Journal of Computer-Aided Molecular Design. 31(1). 71–85. 13 indexed citations
19.
Heo, Lim, Hasup Lee, Minkyung Baek, & Chaok Seok. (2016). Binding Site Prediction of Proteins with Organic Compounds or Peptides Using GALAXY Web Servers. Methods in molecular biology. 1414. 33–45. 6 indexed citations
20.
Chung, Ho Young, et al.. (2008). Detection of SNPs in Porcine Haptoglobin and Apolipoprotein Genes. Biochemical Genetics. 46(11-12). 744–754. 2 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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