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 36
- Co-authors
- Tingjun Hou (40 shared papers)Dongsheng Cao (24 shared papers)Zhenhua Wu (17 shared papers)Chang‐Yu Hsieh (24 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
48 papers receiving 1.7k citations
Dejun Jiang's Hit Papers
Peers
Comparison fields: 5 of 131
- Computational Theory and Mathematics 1.1k
- Materials Chemistry 634
- Molecular Biology 914
- Biophysics 38
- Pharmacology 109
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 54 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 | 388 |
| 2 | 2021 | 147 | |
| 3 | 2021 | 122 | |
| 4 | 2020 | 122 | |
| 5 | 2023 | 89 | |
| 6 | 2021 | 72 | |
| 7 | 2022 | 65 | |
| 8 | 2020 | 59 | |
| 9 | 2021 | 52 | |
| 10 | 2021 | 39 | |
| 11 | 2021 | 37 | |
| 12 | 2023 | 36 | |
| 13 | 2022 | 34 | |
| 14 | 2021 | 27 | |
| 15 | 2001 | 24 | |
| 16 | 2023 | 21 | |
| 17 | 2022 | 21 | |
| 18 | 2022 | 20 | |
| 19 | 2024 | 20 | |
| 20 | 2023 | 19 |
About Dejun Jiang
Dejun Jiang is a scholar working on Molecular Biology, Computational Theory and Mathematics, Materials Chemistry, Organic Chemistry and Oncology, having authored 54 papers that have together received 1.7k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (36 papers), Protein Structure and Dynamics (19 papers), Machine Learning in Materials Science (18 papers), Chemical Synthesis and Analysis (7 papers), RNA and protein synthesis mechanisms (5 papers), Click Chemistry and Applications (5 papers), vaccines and immunoinformatics approaches (3 papers) and Monoclonal and Polyclonal Antibodies Research (3 papers). The work is most often cited by research in Computational Theory and Mathematics (1.1k citations), Materials Chemistry (634 citations), Molecular Biology (914 citations), Biophysics (38 citations) and Pharmacology (109 citations). Dejun Jiang has collaborated with scholars based in China, Macao and Hong Kong. Frequent co-authors include Tingjun Hou, Dongsheng Cao, Zhenhua Wu, Chang‐Yu Hsieh, Jike Wang, Zhe Wang, Chao Shen, Ben Liao, Guangyong Chen and Yu Kang. Their work appears in journals such as Briefings in Bioinformatics, Journal of Chemical Information and Modeling, Nature Communications, Chemical Science and Journal of Medicinal 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.