Dejun Jiang
Impact in
- Computational Theory and Mathematics top 0.5%
- Computational Drug Discovery Methods
- Materials Chemistry top 10%
- Machine Learning in Materials Science
Papers in
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- Protein Structure and Dynamics 19
- Chemical Synthesis and Analysis 7
- RNA and protein synthesis mechanisms 5
- vaccines and immunoinformatics approaches 3
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- Computational Drug Discovery Methods 37
- Co-authors
- Tingjun Hou (42 shared papers)Dongsheng Cao (25 shared papers)Chang‐Yu Hsieh (24 shared papers)Zhenhua Wu (15 shared papers)Jike Wang (20 shared papers)Zhe Wang (7 shared papers)Chao Shen (10 shared papers)Ben Liao (5 shared papers)
In The Last Decade
Dejun Jiang
51 papers receiving 1.9k citations
Dejun Jiang's Hit Papers
Peers
Comparison fields: 5 of 131
- Computational Theory and Mathematics 1.2k
- Materials Chemistry 663
- Molecular Biology 932
- Biophysics 43
- Pharmacology 108
Countries citing papers authored by Dejun Jiang
This map shows the geographic impact of Dejun Jiang'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 Dejun Jiang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dejun Jiang more than expected).
Fields of papers citing papers by Dejun Jiang
This network shows the impact of papers produced by Dejun Jiang. 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 Dejun Jiang. The network helps show where Dejun Jiang may publish in the future.
Co-authors
The 25 scholars most cited alongside Dejun Jiang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 55 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models Hit paper breakdown → | 2021 | 422 |
| 2 | 2021 | 162 | |
| 3 | 2020 | 139 | |
| 4 | 2021 | 139 | |
| 5 | 2023 | 108 | |
| 6 | 2021 | 77 | |
| 7 | 2022 | 67 | |
| 8 | 2020 | 64 | |
| 9 | 2021 | 53 | |
| 10 | 2021 | 42 | |
| 11 | 2021 | 40 | |
| 12 | 2023 | 38 | |
| 13 | 2022 | 36 | |
| 14 | 2021 | 29 | |
| 15 | 2024 | 27 | |
| 16 | 2023 | 25 | |
| 17 | 2001 | 24 | |
| 18 | 2022 | 23 | |
| 19 | 2022 | 22 | |
| 20 | 2024 | 21 |
About Dejun Jiang
Dejun Jiang is a scholar working on Molecular Biology, Computational Theory and Mathematics, Materials Chemistry, Organic Chemistry and Oncology, having authored 55 papers that have together received 1.9k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (37 papers), Protein Structure and Dynamics (19 papers), Machine Learning in Materials Science (18 papers), Chemical Synthesis and Analysis (7 papers), Click Chemistry and Applications (6 papers), RNA and protein synthesis mechanisms (5 papers), Monoclonal and Polyclonal Antibodies Research (3 papers) and vaccines and immunoinformatics approaches (3 papers). The work is most often cited by research in Computational Theory and Mathematics (1.2k citations), Materials Chemistry (663 citations), Molecular Biology (932 citations), Biophysics (43 citations) and Pharmacology (108 citations). Dejun Jiang has collaborated with scholars based in China, Macao and Hong Kong. Frequent co-authors include Tingjun Hou, Dongsheng Cao, Chang‐Yu Hsieh, Zhenhua Wu, Jike Wang, Zhe Wang, Chao Shen, Ben Liao, Yu Kang and Guangyong Chen. Their work appears in journals such as Journal of Chemical Information and Modeling, Briefings in Bioinformatics, Chemical Science, Journal of Medicinal Chemistry and Nature Communications.
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.