Dejun Jiang
Impact in
- Computational Theory and Mathematics top 0.2%
- Computational Drug Discovery Methods
- Materials Chemistry top 10%
- Machine Learning in Materials Science
Papers in
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- Computational Drug Discovery Methods 47
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- Protein Structure and Dynamics 4
- Machine Learning in Bioinformatics 4
- vaccines and immunoinformatics approaches 3
- Developmental Biology and Gene Regulation 2
- Co-authors
- Tingjun Hou (43 shared papers)Dongsheng Cao (27 shared papers)Chang‐Yu Hsieh (24 shared papers)Ben Liao (6 shared papers)Jike Wang (20 shared papers)Chao Shen (11 shared papers)Zhe Wang (7 shared papers)Yu Kang (18 shared papers)
In The Last Decade
Dejun Jiang
54 papers receiving 2.1k citations
Dejun Jiang's Hit Papers
Peers
Comparison fields: 5 of 122
- Computational Theory and Mathematics 1.5k
- Materials Chemistry 221
- Health Informatics 11
- Molecular Biology 222
- Artificial Intelligence 122
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 58 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 | 463 |
| 2 | InteractionGraphNet: A Novel and Efficient Deep Graph Representation Learning Framework for Accurate Protein–Ligand Interaction Predictions Hit paper breakdown → | 2021 | 189 |
| 3 | 2020 | 153 | |
| 4 | 2021 | 153 | |
| 5 | Chemistry-intuitive explanation of graph neural networks for molecular property prediction with substructure masking Hit paper breakdown → | 2023 | 131 |
| 6 | 2021 | 89 | |
| 7 | 2022 | 76 | |
| 8 | 2020 | 67 | |
| 9 | 2021 | 60 | |
| 10 | 2021 | 47 | |
| 11 | 2022 | 45 | |
| 12 | 2023 | 44 | |
| 13 | 2021 | 42 | |
| 14 | 2023 | 31 | |
| 15 | 2021 | 30 | |
| 16 | 2024 | 29 | |
| 17 | 2023 | 26 | |
| 18 | 2024 | 26 | |
| 19 | 2001 | 25 | |
| 20 | 2022 | 24 |
About Dejun Jiang
Dejun Jiang is a scholar working on Computational Theory and Mathematics, Molecular Biology, Toxicology, Materials Chemistry and Artificial Intelligence, having authored 58 papers that have together received 2.1k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (47 papers), Protein Structure and Dynamics (4 papers), Machine Learning in Bioinformatics (4 papers), Machine Learning in Materials Science (3 papers), vaccines and immunoinformatics approaches (3 papers), Developmental Biology and Gene Regulation (2 papers), Pharmacovigilance and Adverse Drug Reactions (1 paper) and Explainable Artificial Intelligence (XAI) (1 paper). The work is most often cited by research in Computational Theory and Mathematics (1.5k citations), Materials Chemistry (221 citations), Health Informatics (11 citations), Molecular Biology (222 citations) and Artificial Intelligence (122 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, Ben Liao, Jike Wang, Chao Shen, Zhe Wang, Yu Kang, Guang-Yong Chen and Jian Wu. Their work appears in journals such as Journal of Chemical Information and Modeling, Briefings in Bioinformatics, Nature Communications, Journal of Medicinal Chemistry and Chemical Science.
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.