Youngchun Kwon
Impact in
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- Computational Drug Discovery Methods
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- Machine Learning in Materials Science
Papers in
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- Computational Drug Discovery Methods 14
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- Machine Learning in Materials Science 11
- Co-authors
- Youn-Suk Choi (15 shared papers)Seokho Kang (12 shared papers)Dongseon Lee (11 shared papers)Jiho Yoo (2 shared papers)Inkoo Kim (2 shared papers)Won‐Joon Son (2 shared papers)Eunji Kim (2 shared papers)Min‐Sik Park (1 shared paper)
- Journals
- Journal of Cheminformatics (4 papers)Journal of Chemical Information and Modeling (4 papers)Scientific Reports (2 papers)Physical Chemistry Chemical Physics (2 papers)npj Computational Materials (1 paper)
- Partner nations
- South Korea
In The Last Decade
Youngchun Kwon
15 papers receiving 447 citations
Peers
Comparison fields: 5 of 78
- Computational Theory and Mathematics 257
- Materials Chemistry 279
- Spectroscopy 66
- Molecular Biology 201
- Physical and Theoretical Chemistry 19
Countries citing papers authored by Youngchun Kwon
This map shows the geographic impact of Youngchun Kwon'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 Youngchun Kwon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Youngchun Kwon more than expected).
Fields of papers citing papers by Youngchun Kwon
This network shows the impact of papers produced by Youngchun Kwon. 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 Youngchun Kwon. The network helps show where Youngchun Kwon may publish in the future.
Co-authors
The 16 scholars most cited alongside Youngchun Kwon, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2018 | 116 | |
| 2 | 2019 | 47 | |
| 3 | 2020 | 46 | |
| 4 | 2022 | 45 | |
| 5 | 2021 | 43 | |
| 6 | 2021 | 30 | |
| 7 | 2020 | 26 | |
| 8 | 2020 | 23 | |
| 9 | 2022 | 18 | |
| 10 | 2022 | 17 | |
| 11 | 2022 | 15 | |
| 12 | 2023 | 14 | |
| 13 | 2024 | 10 | |
| 14 | 2021 | 7 | |
| 15 | 2021 | 2 |
About Youngchun Kwon
Youngchun Kwon is a scholar working on Computational Theory and Mathematics, Materials Chemistry, Molecular Biology, Spectroscopy and Artificial Intelligence, having authored 15 papers that have together received 459 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (14 papers), Machine Learning in Materials Science (11 papers), Molecular spectroscopy and chirality (4 papers), Metabolomics and Mass Spectrometry Studies (3 papers), Machine Learning in Bioinformatics (2 papers), Protein Structure and Dynamics (2 papers), Advanced Graph Neural Networks (1 paper) and Conducting polymers and applications (1 paper). The work is most often cited by research in Computational Theory and Mathematics (257 citations), Materials Chemistry (279 citations), Spectroscopy (66 citations), Molecular Biology (201 citations) and Physical and Theoretical Chemistry (19 citations). Youngchun Kwon has collaborated with scholars based in South Korea. Frequent co-authors include Youn-Suk Choi, Seokho Kang, Dongseon Lee, Jiho Yoo, Inkoo Kim, Won‐Joon Son, Eunji Kim, Min‐Sik Park, Young Min Nam and Yongsik Jung. Their work appears in journals such as Journal of Cheminformatics, Journal of Chemical Information and Modeling, Scientific Reports, Physical Chemistry Chemical Physics and npj Computational Materials.
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.