Youngchun Kwon

803 total citations
15 papers, 459 citations indexed

About

Youngchun Kwon is a scholar working on Computational Theory and Mathematics, Materials Chemistry and Molecular Biology. According to data from OpenAlex, Youngchun Kwon has authored 15 papers receiving a total of 459 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Computational Theory and Mathematics, 11 papers in Materials Chemistry and 7 papers in Molecular Biology. Recurrent topics in Youngchun Kwon's work include Computational Drug Discovery Methods (14 papers), Machine Learning in Materials Science (11 papers) and Molecular spectroscopy and chirality (4 papers). Youngchun Kwon is often cited by papers focused on Computational Drug Discovery Methods (14 papers), Machine Learning in Materials Science (11 papers) and Molecular spectroscopy and chirality (4 papers). Youngchun Kwon collaborates with scholars based in South Korea. Youngchun Kwon's co-authors include Youn-Suk Choi, Seokho Kang, Dongseon Lee, Jiho Yoo, Won‐Joon Son, Inkoo Kim, Min‐Sik Park, Eunji Kim, Sangmo Kim and Yongsik Jung and has published in prestigious journals such as Analytical Chemistry, Scientific Reports and Physical Chemistry Chemical Physics.

In The Last Decade

Youngchun Kwon

15 papers receiving 447 citations

Peers

Youngchun Kwon

CTM

SPECT

Riccardo Petraglia Switzerland

Kevin P. Greenman United States

Mojtaba Haghighatlari United States

Charles J. McGill United States

Adam C. Mater Australia

Lagnajit Pattanaik United States

Justin Gilmer United States

Zois Boukouvalas United States

Sabrina Jaeger-Honz Germany

Michał D. Bajczyk Poland

Riccardo Petraglia Switzerland

Youngchun Kwon

263 ×0.9

170 ×0.7

CTM

131 ×0.7

29 ×0.4

SPECT

63 ×1.2

Citations per year, relative to Youngchun Kwon Youngchun Kwon (= 1×) peers Riccardo Petraglia

Countries citing papers authored by Youngchun Kwon

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Youngchun Kwon

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

All Works

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15 of 15 papers shown

Kwon, Youngchun, et al.. (2024). Improving chemical reaction yield prediction using pre-trained graph neural networks. Journal of Cheminformatics. 16(1). 25–25. 10 indexed citations

Kwon, Youngchun, et al.. (2023). Retention Time Prediction through Learning from a Small Training Data Set with a Pretrained Graph Neural Network. Analytical Chemistry. 95(47). 17273–17283. 14 indexed citations

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

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

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

Kwon, Youngchun, et al.. (2022). Generative Modeling to Predict Multiple Suitable Conditions for Chemical Reactions. Journal of Chemical Information and Modeling. 62(23). 5952–5960. 15 indexed citations

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

Kwon, Youngchun, Seokho Kang, Youn-Suk Choi, & Inkoo Kim. (2021). Evolutionary design of molecules based on deep learning and a genetic algorithm. Scientific Reports. 11(1). 17304–17304. 30 indexed citations

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

10.

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

11.

Kwon, Youngchun, et al.. (2020). Compressed graph representation for scalable molecular graph generation. Journal of Cheminformatics. 12(1). 58–58. 26 indexed citations

12.

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

13.

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

14.

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

15.

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

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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