Dongseon Lee

1.3k total citations
19 papers, 893 citations indexed

About

Dongseon Lee is a scholar working on Materials Chemistry, Computational Theory and Mathematics and Molecular Biology. According to data from OpenAlex, Dongseon Lee has authored 19 papers receiving a total of 893 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Materials Chemistry, 10 papers in Computational Theory and Mathematics and 8 papers in Molecular Biology. Recurrent topics in Dongseon Lee's work include Computational Drug Discovery Methods (10 papers), Machine Learning in Materials Science (7 papers) and Protein Structure and Dynamics (4 papers). Dongseon Lee is often cited by papers focused on Computational Drug Discovery Methods (10 papers), Machine Learning in Materials Science (7 papers) and Protein Structure and Dynamics (4 papers). Dongseon Lee collaborates with scholars based in South Korea, China and Japan. Dongseon Lee's co-authors include Chaok Seok, Youn-Suk Choi, Youngchun Kwon, Seokho Kang, Myongsoo Lee, Motonori Banno, Eiji Yashima, Zhegang Huang, Tomoko Yamaguchi and Won‐Joon Son and has published in prestigious journals such as Science, Angewandte Chemie International Edition and Scientific Reports.

In The Last Decade

Dongseon Lee

18 papers receiving 877 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Dongseon Lee South Korea 14 481 319 289 272 239 19 893
Riccardo Capelli Italy 15 298 0.6× 399 1.3× 191 0.7× 211 0.8× 95 0.4× 45 823
Charly Empereur‐mot Switzerland 10 260 0.5× 358 1.1× 277 1.0× 251 0.9× 94 0.4× 14 925
Chris M. Gothard United States 12 261 0.5× 250 0.8× 168 0.6× 154 0.6× 182 0.8× 16 617
Jonathan N. Jaworski United States 11 458 1.0× 352 1.1× 84 0.3× 367 1.3× 220 0.9× 12 1.2k
Mircea V. Diudea Romania 22 446 0.9× 164 0.5× 133 0.5× 864 3.2× 704 2.9× 166 1.7k
Wenhui Xi China 17 230 0.5× 652 2.0× 263 0.9× 136 0.5× 121 0.5× 37 1.0k
Luming Meng China 17 957 2.0× 455 1.4× 189 0.7× 536 2.0× 48 0.2× 27 1.5k
Hiroko Satoh Japan 18 267 0.6× 482 1.5× 31 0.1× 436 1.6× 180 0.8× 64 1.1k
Rubén Laplaza Switzerland 15 410 0.9× 171 0.5× 27 0.1× 305 1.1× 188 0.8× 46 890
Gaurav Sharma United States 17 248 0.5× 188 0.6× 63 0.2× 111 0.4× 39 0.2× 46 668

Countries citing papers authored by Dongseon Lee

Since Specialization
Citations

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

Fields of papers citing papers by Dongseon Lee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dongseon Lee

This figure shows the co-authorship network connecting the top 25 collaborators of Dongseon Lee. A scholar is included among the top collaborators of Dongseon Lee 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 Dongseon Lee. Dongseon Lee 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.
Kang, Hyungu, et al.. (2022). Scalable graph neural network for NMR chemical shift prediction. Physical Chemistry Chemical Physics. 24(43). 26870–26878. 17 indexed citations
2.
Kwon, Youngchun, Dongseon Lee, Youn-Suk Choi, & Seokho Kang. (2022). Uncertainty-aware prediction of chemical reaction yields with graph neural networks. Journal of Cheminformatics. 14(1). 2–2. 45 indexed citations
3.
Kwon, Youngchun, et al.. (2022). Exploring Optimal Reaction Conditions Guided by Graph Neural Networks and Bayesian Optimization. ACS Omega. 7(49). 44939–44950. 18 indexed citations
4.
Kim, Eunji, Dongseon Lee, Youngchun Kwon, Min‐Sik Park, & Youn-Suk Choi. (2021). Valid, Plausible, and Diverse Retrosynthesis Using Tied Two-Way Transformers with Latent Variables. Journal of Chemical Information and Modeling. 61(1). 123–133. 43 indexed citations
5.
Lee, Dongseon, et al.. (2021). Data undersampling models for the efficient rule-based retrosynthetic planning. Physical Chemistry Chemical Physics. 23(46). 26510–26518. 2 indexed citations
6.
Kwon, Youngchun, Dongseon Lee, Youn-Suk Choi, & Seokho Kang. (2021). Molecular search by NMR spectrum based on evaluation of matching between spectrum and molecule. Scientific Reports. 11(1). 20998–20998. 7 indexed citations
7.
Lee, Dongseon, Jeongil Choi, & Ju‐Young M. Kang. (2021). A Study on the Factors Affecting the Intention to Use VR-based Digital Content Subscription Service. 25(2). 135–162. 3 indexed citations
8.
Kwon, Youngchun, et al.. (2020). Neural Message Passing for NMR Chemical Shift Prediction. Journal of Chemical Information and Modeling. 60(4). 2024–2030. 46 indexed citations
9.
Kwon, Youngchun, et al.. (2020). Compressed graph representation for scalable molecular graph generation. Journal of Cheminformatics. 12(1). 58–58. 26 indexed citations
10.
Kang, Seokho, Youngchun Kwon, Dongseon Lee, & Youn-Suk Choi. (2020). Predictive Modeling of NMR Chemical Shifts without Using Atomic-Level Annotations. Journal of Chemical Information and Modeling. 60(8). 3765–3769. 23 indexed citations
11.
Kwon, Youngchun, Jiho Yoo, Youn-Suk Choi, et al.. (2019). Efficient learning of non-autoregressive graph variational autoencoders for molecular graph generation. Journal of Cheminformatics. 11(1). 70–70. 47 indexed citations
12.
Kim, Inkoo, Won‐Joon Son, Youn-Suk Choi, et al.. (2019). Predicting Phosphorescence Quantum Yield for Pt(II)-Based OLED Emitters from Correlation Function Approach. The Journal of Physical Chemistry C. 123(17). 11140–11150. 11 indexed citations
13.
Kang, Seokho, Jiho Yoo, Youngchun Kwon, et al.. (2018). Deep-learning-based inverse design model for intelligent discovery of organic molecules. npj Computational Materials. 4(1). 116 indexed citations
14.
Seo, Eun-Joo, Chulmin Park, Kyuha Choi, et al.. (2016). Molecular evolution of ACTIN RELATED PROTEIN 6, a component of SWR1 complex in Arabidopsis. Journal of Plant Biology. 59(5). 467–477. 2 indexed citations
15.
Lee, Dongseon, Juyong Lee, & Chaok Seok. (2013). What stabilizes close arginine pairing in proteins?. Physical Chemistry Chemical Physics. 15(16). 5844–5844. 41 indexed citations
16.
Kim, Yongju, Suyong Shin, Tae‐Hoon Kim, et al.. (2013). Switchable Nanoporous Sheets by the Aqueous Self‐Assembly of Aromatic Macrobicycles. Angewandte Chemie International Edition. 52(25). 6426–6429. 58 indexed citations
17.
Kim, Yongju, Suyong Shin, Tae Whan Kim, et al.. (2013). Switchable Nanoporous Sheets by the Aqueous Self‐Assembly of Aromatic Macrobicycles. Angewandte Chemie. 125(25). 6554–6557. 21 indexed citations
18.
Huang, Zhegang, Motonori Banno, Tomoko Yamaguchi, et al.. (2012). Pulsating Tubules from Noncovalent Macrocycles. Science. 337(6101). 1521–1526. 291 indexed citations
19.
Lee, Julian, Dongseon Lee, Hahnbeom Park, Evangelos A. Coutsias, & Chaok Seok. (2010). Protein loop modeling by using fragment assembly and analytical loop closure. Proteins Structure Function and Bioinformatics. 78(16). 3428–3436. 76 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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