Junsu Ko
Impact in
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- Computational Drug Discovery Methods
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- Protein Structure and Dynamics
- Bioinformatics and Genomic Networks
- Chemical Synthesis and Analysis
- Microbial Metabolic Engineering and Bioproduction
- Genetics, Bioinformatics, and Biomedical Research
Papers in
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- Protein Structure and Dynamics 3
- Enzyme function and inhibition 1
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- Computational Drug Discovery Methods 5
- Co-authors
- Juyong Lee (8 shared papers)Woong‐Hee Shin (5 shared papers)Jooyoung Lee (1 shared paper)Chaok Seok (1 shared paper)Taek Kang (2 shared papers)Lim Heo (1 shared paper)Sanghee Lee (2 shared papers)Ji‐Hye Oh (1 shared paper)
- Journals
- Journal of Cheminformatics (2 papers)Scientific Reports (2 papers)International Journal of Molecular Sciences (1 paper)Bioorganic & Medicinal Chemistry (1 paper)Journal of Computational Chemistry (1 paper)
- Partner nations
- South KoreaJapanPuerto Rico
In The Last Decade
Junsu Ko
9 papers receiving 213 citations
Peers
Comparison fields: 5 of 52
- Computational Theory and Mathematics 151
- Molecular Biology 145
- Materials Chemistry 93
- Pharmacology 23
- Software 3
Countries citing papers authored by Junsu Ko
This map shows the geographic impact of Junsu Ko'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 Junsu Ko with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Junsu Ko more than expected).
Fields of papers citing papers by Junsu Ko
This network shows the impact of papers produced by Junsu Ko. 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 Junsu Ko. The network helps show where Junsu Ko may publish in the future.
Co-authors
The 25 scholars most cited alongside Junsu Ko, 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 | 2020 | 87 | |
| 2 | 2022 | 57 | |
| 3 | 2011 | 35 | |
| 4 | 2021 | 22 | |
| 5 | 2024 | 6 | |
| 6 | 2024 | 5 | |
| 7 | 2022 | 3 | |
| 8 | 2024 | 1 | |
| 9 | 2023 | 1 |
About Junsu Ko
Junsu Ko is a scholar working on Molecular Biology, Computational Theory and Mathematics, Materials Chemistry, Pharmacology and Oncology, having authored 9 papers that have together received 217 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (5 papers), Protein Structure and Dynamics (3 papers), Microbial Natural Products and Biosynthesis (2 papers), Luminescence and Fluorescent Materials (1 paper), Cancer Genomics and Diagnostics (1 paper), Lymphatic System and Diseases (1 paper), Natural Language Processing Techniques (1 paper) and Enzyme function and inhibition (1 paper). The work is most often cited by research in Computational Theory and Mathematics (151 citations), Molecular Biology (145 citations), Materials Chemistry (93 citations), Pharmacology (23 citations) and Software (3 citations). Junsu Ko has collaborated with scholars based in South Korea, Japan and Puerto Rico. Frequent co-authors include Juyong Lee, Woong‐Hee Shin, Jooyoung Lee, Chaok Seok, Taek Kang, Lim Heo, Sanghee Lee, Ji‐Hye Oh, Cheulhee Jung and Sung‐Yup Cho. Their work appears in journals such as Journal of Cheminformatics, Scientific Reports, International Journal of Molecular Sciences, Bioorganic & Medicinal Chemistry and Journal of Computational Chemistry.
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.