Junsu Ko

622 total citations
9 papers, 217 citations indexed

About

Junsu Ko is a scholar working on Molecular Biology, Computational Theory and Mathematics and Materials Chemistry. According to data from OpenAlex, Junsu Ko has authored 9 papers receiving a total of 217 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Molecular Biology, 5 papers in Computational Theory and Mathematics and 3 papers in Materials Chemistry. Recurrent topics in Junsu Ko's work include Computational Drug Discovery Methods (5 papers), Protein Structure and Dynamics (3 papers) and Microbial Natural Products and Biosynthesis (2 papers). Junsu Ko is often cited by papers focused on Computational Drug Discovery Methods (5 papers), Protein Structure and Dynamics (3 papers) and Microbial Natural Products and Biosynthesis (2 papers). Junsu Ko collaborates with scholars based in South Korea, Japan and Ethiopia. Junsu Ko's co-authors include Juyong Lee, Woong‐Hee Shin, Taek Kang, Jooyoung Lee, Lim Heo, Chaok Seok, Sanghee Lee, Insuk Sohn, Chang Ohk Sung and Sung‐Yup Cho and has published in prestigious journals such as Nature Communications, Scientific Reports and International Journal of Molecular Sciences.

In The Last Decade

Junsu Ko

9 papers receiving 213 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Junsu Ko South Korea	6	151	145	93	23	14	9	217
Jintu Zhang China	9	160 1.1×	181 1.2×	77 0.8×	21 0.9×	11 0.8×	15	271
Łukasz Maziarka Poland	5	147 1.0×	105 0.7×	117 1.3×	10 0.4×	27 1.9×	11	213
Talia B. Kimber Germany	5	162 1.1×	122 0.8×	80 0.9×	10 0.4×	20 1.4×	5	216
Alejandro Varela‐Rial Spain	6	109 0.7×	114 0.8×	76 0.8×	10 0.4×	6 0.4×	8	183
Bulat Zagidullin Finland	4	161 1.1×	155 1.1×	43 0.5×	18 0.8×	18 1.3×	7	235
Odin Zhang China	11	191 1.3×	203 1.4×	87 0.9×	20 0.9×	19 1.4×	27	328
Jeff Guo Sweden	7	133 0.9×	107 0.7×	126 1.4×	8 0.3×	6 0.4×	10	233
Yo Joong Choe United States	3	206 1.4×	210 1.4×	115 1.2×	24 1.0×	50 3.6×	4	304
Xiaochu Tong China	7	185 1.2×	164 1.1×	82 0.9×	21 0.9×	18 1.3×	14	293
Soojung Yang South Korea	2	119 0.8×	93 0.6×	58 0.6×	20 0.9×	10 0.7×	3	142

Countries citing papers authored by Junsu Ko

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Junsu Ko

This figure shows the co-authorship network connecting the top 25 collaborators of Junsu Ko. A scholar is included among the top collaborators of Junsu Ko 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 Junsu Ko. Junsu Ko is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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9 of 9 papers shown

1.

Kim, Jong Seung, Cheulhee Jung, Nam‐Jung Kim, et al.. (2024). Discovery of thiophen-2-ylmethylene bis-dimedone derivatives as novel WRN inhibitors for treating cancers with microsatellite instability. Bioorganic & Medicinal Chemistry. 100. 117588–117588. 6 indexed citations

2.

Lee, Juyong, et al.. (2024). Improving docking and virtual screening performance using AlphaFold2 multi-state modeling for kinases. Scientific Reports. 14(1). 25167–25167. 1 indexed citations

3.

Hong, Yiyu, Chae Jo Lim, Kwang‐Seok Oh, et al.. (2024). Accurate prediction of protein–ligand interactions by combining physical energy functions and graph-neural networks. Journal of Cheminformatics. 16(1). 121–121. 5 indexed citations

4.

Oh, Ji‐Hye, Junsu Ko, Eunsung Jun, et al.. (2023). Intercellular cross-talk through lineage-specific gap junction of cancer-associated fibroblasts related to stromal fibrosis and prognosis. Scientific Reports. 13(1). 14230–14230. 1 indexed citations

5.

Ko, Junsu, et al.. (2022). Retrosynthetic reaction pathway prediction through neural machine translation of atomic environments. Nature Communications. 13(1). 1186–1186. 57 indexed citations

6.

Lee, Jaeseok, Sang-Kee Choi, Adil S. Aslam, et al.. (2022). N4-phenylquinazoline-4,6-diamine as a tunable fluorescent scaffold for the development of fluorescent probes. Dyes and Pigments. 210. 110987–110987. 3 indexed citations

7.

Kang, Taek, et al.. (2021). Substructure-based neural machine translation for retrosynthetic prediction. Journal of Cheminformatics. 13(1). 4–4. 22 indexed citations

8.

Shin, Woong‐Hee, et al.. (2020). AK-Score: Accurate Protein-Ligand Binding Affinity Prediction Using an Ensemble of 3D-Convolutional Neural Networks. International Journal of Molecular Sciences. 21(22). 8424–8424. 87 indexed citations

9.

Shin, Woong‐Hee, Lim Heo, Juyong Lee, et al.. (2011). LigDockCSA: Protein–ligand docking using conformational space annealing. Journal of Computational Chemistry. 32(15). 3226–3232. 35 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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